Dynamically modifying digital content distribution campaigns based on triggering conditions and actions

ABSTRACT

The present disclosure is directed toward systems, methods, and non-transitory computer readable media that dynamically modify content distribution campaigns based on triggering conditions and actions. In particular, systems described herein can provide a user interface for display to a publisher device that includes a plurality of selectable options for setting triggering conditions and/or actions. For example, the disclosed systems can utilize a machine learning model to generate suggested triggering conditions and/or actions for one or more content distribution campaigns of a provider. Moreover, the disclosed systems can generate custom rules based on selected triggering conditions and actions and apply the custom rules during execution of digital content campaigns. For instance, the disclosed systems can monitor performance of content campaigns, detect triggering conditions, and dynamically modify digital content campaigns based on actions corresponding to the triggering conditions.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. application Ser. No.16/890,451, filed on Jun. 2, 2020, which is a continuation of U.S.application Ser. No. 15/798,170, filed on Oct. 30, 2017, which issued asU.S. Pat. No. 10,692,106. Each of the aforementioned applications ishereby incorporated by reference in its entirety.

BACKGROUND

Recent years have seen significant advances in hardware and softwareplatforms for disseminating content to remote client devices throughdigital content distribution systems. For instance, publishers can nowseamlessly execute content distribution campaigns for entertainment,advertising, or business purposes by distributing digital content overthe Internet to a plurality of client devices corresponding to varioustarget audiences.

Although conventional content distribution systems allow individuals andbusinesses to disseminate digital content through computer networks,they also have a number of drawbacks. For example, conventional contentdistribution systems are generally rigid, and provide little flexibilityduring execution of a digital content campaign to respond to changingconditions. To illustrate, conventional content distribution systemsoften require publishers to create custom pipelines that periodicallyquery conventional content distribution systems (e.g. via an API)regarding performance of a digital content distribution campaign. Then,based on information regarding the performance of the digital contentdistribution campaign, conventional content distribution systems oftenrequire merchants to manually adjust various campaign parameters toeffectuate a change to the content distribution campaign.

This approach is inefficient for both publishers and computing systemsimplementing conventional content distribution systems. Indeed,conventional content distribution systems expend significant computingresources to repeatedly respond to queries and manage requests formodifications from various publishers. Similarly, publishers expendsignificant computing resources submitting queries, analyzingperformance data, and manually requesting modifications. Moreover,conventional content distribution systems lead to slower system responsetimes, delayed performance data, and inefficient and delayedmodifications to content distribution campaigns.

These and other problems exist regarding conventional contentdistribution systems for distributing content to client devices overcomputer networks.

SUMMARY

One or more embodiments described below provide benefits and/or solveone or more of the foregoing or other problems in the art with systems,methods, and computer readable media that dynamically modify a contentdistribution campaign in real time during execution of the contentdistribution campaign based on selectable triggering conditions andcorresponding actions. For instance, in one or more embodiments, thedisclosed systems provide a user interface that allows publishers toselect triggering conditions and corresponding actions to perform duringa content distribution campaign. Indeed, in one or more embodiments, thedisclosed systems generate suggested triggering conditions andcorresponding actions (utilizing a machine learning model) to assist theuser in improving performance of a content distribution campaign. Thedisclosed systems can then utilize selected triggering conditions andactions to notify publishers regarding changes during a digital contentcampaign and to dynamically adjust digital content campaigns in realtime.

To illustrate, in one or more embodiments, the disclosed systems providea user interface comprising a plurality of selectable triggeringconditions and actions corresponding to a content distribution campaign(e.g., suggested triggering conditions and actions generated by amachine learning model). Furthermore, the disclosed systems generate acustom rule for modifying the content distribution campaign based ontriggering conditions and actions selected via the user interface. Onexecuting the content distribution campaign, the disclosed systemsmonitor activity corresponding to the content distribution campaign todetect satisfaction of the triggering conditions. In response todetecting satisfaction of the triggering conditions, the disclosedsystems automatically modify the content distribution campaign accordingto the actions in the custom rule. Thereafter, the disclosed systemsautomatically execute the modified content distribution campaign.

The disclosed systems provide a number of advantages over conventionalcontent distribution systems. Indeed, the disclosed systems provideincreased flexibility by providing a plurality of selectable triggeringconditions and actions (e.g., suggested triggering conditions andactions identified by a machine learning model trained to identifytriggering conditions and actions most likely to improve performance ofa particular campaign). In particular, the disclosed systems can provideselectable triggering conditions and actions that publishers can utilizeto uniquely tailor execution, modifications, and notifications during adigital content campaign. Furthermore, the disclosed systems can improveefficiency for implementing computing devices. Indeed, the disclosedsystems can significantly reduce the computing resources expended byconventional systems to repeatedly respond to queries from publishersand manage modification requests. Similarly, the disclosed systems cansignificantly reduce the need for publishers to expend computingresources to submit queries, analyze performance data, and manuallymodify campaign parameters. Furthermore, by automatically modifyingcontent distribution campaigns and notifying publishers based onpreviously selected triggering conditions and actions, the disclosedsystems can improve response time, reduce delay, and provide efficient,timely modifications to content distribution campaigns.

Additional features and advantages will be set forth in the descriptionwhich follows, and in part will be obvious from the description, or maybe learned by the practice of such exemplary embodiments. The featuresand advantages of such embodiments may be realized and obtained by meansof the instruments and combinations particularly pointed out in theappended claims. These and other features will become more fullyapparent from the following description and appended claims, or may belearned by the practice of such exemplary embodiments as set forthhereinafter.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments will be described and explained with additionalspecificity and detail through the use of the accompanying drawings inwhich:

FIG. 1 illustrates a schematic diagram of a network environment in whicha digital campaign modification system can be implemented in accordancewith one or more embodiments;

FIGS. 2A-2B illustrate a sequence diagram of a plurality of acts in amethod of dynamically modifying a content distribution campaign inaccordance with one or more embodiments;

FIGS. 3A-3D illustrate a computing device and user interface forgenerating a custom rule in accordance with one or more embodiments;

FIG. 4 illustrates a computing device and user interface for generatinga custom rule, including suggested triggering conditions and actions inaccordance with one or more embodiments;

FIG. 5 illustrates a computing device and user interface for managing acontent distribution campaign and suggested rules in accordance with oneor more embodiments;

FIG. 6 illustrates a computing device and notification for indicatingautomatic modification of a content distribution campaign in accordancewith one or more embodiments;

FIG. 7 illustrates an example sequence flow for training a machinelearning model in accordance with one or more embodiments;

FIG. 8 illustrates an example sequence flow for applying a trainedmachine learning model to generate suggested rules in accordance withone or more embodiments;

FIG. 9 illustrates a schematic diagram of a digital campaignmodification system in accordance with one or more embodiments;

FIG. 10 illustrates a flow chart of a method of dynamically modifying acontent distribution campaign in accordance with one or moreembodiments;

FIG. 11 illustrates a block diagram of an exemplary computing device inaccordance with one or more embodiments;

FIG. 12 illustrates a network environment of a networking system inaccordance with one or more embodiments; and

FIG. 13 illustrates a social graph in accordance with one or moreembodiments.

DETAILED DESCRIPTION

One or more embodiments of the present invention include a digitalcampaign modification system that dynamically adjusts contentdistribution campaigns in real-time based on custom rules created fromselectable triggering conditions and corresponding actions. Inparticular, in one or more embodiments, the digital campaignmodification system provides a user interface with selectable optionsthat allow digital publishers to customize and automate actions andnotifications based on the performance of content distribution campaignsin real-time. For instance, in one or more embodiments, the digitalcampaign modification system generates suggested triggering conditionsand corresponding actions utilizing a machine learning model trainedbased on historical content distribution campaigns. Accordingly, thedigital campaign modification system provides publishers with the optionto anticipate and automate actions during execution of a contentdistribution campaign without the need for constant manual monitoringand delayed responsive action.

For example, in one or more embodiments, the digital campaignmodification system provides for display, to a publisher device, a userinterface that includes a plurality of triggering conditions and aplurality of actions corresponding to a content distribution campaign.Additionally, in response to user selection of a triggering conditionand user selection of an action, the digital campaign modificationsystem can generate a custom rule for modifying the content distributioncampaign (i.e., a custom rule that reflects the selected triggeringcondition and action). Furthermore, upon executing the contentdistribution campaign, the digital campaign modification system canmonitor activity corresponding to the content distribution campaign todetect satisfaction of the triggering condition. In response todetecting satisfaction of the triggering condition, the digital campaignmodification system can then automatically modify the contentdistribution campaign (e.g., modify one or more campaign parameters)according to the action of the custom rule. Moreover, in one or moreembodiments, the digital campaign modification system then executes themodified content distribution campaign.

As just mentioned, in one or more embodiments, the digital campaignmodification system provides a user interface for display to a publisherdevice that includes a plurality of triggering conditions and actionscorresponding to one or more content distribution campaigns. Inparticular, in one or more embodiments, the digital campaignmodification system provides the user interface to a publisher whensetting up content distribution campaign(s) to provide options forcreating custom rules. To illustrate, the digital campaign modificationsystem can provide a user interface with triggering conditions, such as,a budget threshold (e.g., an amount of budget expenditure), a costthreshold (e.g., a level of cost for purchasing an impressionopportunity to provide digital content), or an impressions threshold(e.g., a number of impression opportunities purchased). Similarly, thedigital campaign modification system can provide a variety of actionsvia the user interface, such as, a modify budget action (e.g., changebudget allocated for a content distribution campaign), a modify digitalcontent action (e.g., change one or more features of digital contentprovided to user devices), a modify bid amount action (e.g., modify bidsfor an online auction to provide digital content to user devices), or apause content distribution campaign action.

Furthermore, in one or more embodiments, the digital campaignmodification system provides a user interface that includes one or morenotification options. For instance, the digital campaign modificationsystem can provide an option to provide a notification, such as, amessage via a networking system or a call to a remote server. Inaddition, the notification option may be coupled with one or moretriggering conditions such that satisfaction of the triggering conditionautomatically provides a notification according to the notificationoption.

As mentioned above, the digital campaign modification system can monitoractivity corresponding to one or more content distribution campaigns. Inparticular, the digital campaign modification system may continuouslymonitor performance of content distribution campaigns to detectsatisfaction of one or more triggering conditions. To illustrate, thedigital campaign modification system can continuously monitor a digitalcontent campaign and detect that a budget expenditure for a contentdistribution campaign has exceeded a threshold corresponding to atriggering condition.

Furthermore, as discussed above, in response to detecting satisfactionof a triggering condition, the digital campaign modification system canautomatically modify content distribution campaigns (and/or providevarious notifications). Specifically, the digital campaign modificationsystem can automatically modify content distribution campaigns accordingto actions corresponding to a triggering condition. To illustrate, upondetecting that a content distribution campaign has exceeded a thresholdbudget expenditure corresponding to a triggering condition, the digitalcampaign modification system can apply an action corresponding to thetriggering condition, such as modifying a bid amount to reduceadditional expenditures.

As mentioned above, in one or more embodiments, the digital campaignmodification system also suggests triggering conditions and/or actions.Specifically, the digital campaign modification system can generate asuggested triggering condition and corresponding suggested actionutilizing a machine learning model. For example, the digital campaignmodification system can train a machine learning model with a pluralityof historical content distribution campaigns. Upon training the machinelearning model, the digital campaign modification system can apply thetrained machine learning model to a new content distribution campaignand provide suggested triggering condition and corresponding suggestedactions for display to a publisher device.

The digital campaign modification system provides a number of advantagesover conventional content distribution systems. For example, the digitalcampaign modification system can increase flexibility in relation toconventional systems. Indeed, by utilizing selectable triggeringconditions and actions, the digital campaign modification system canuniquely tailor execution and modification of digital contentdistribution campaigns to the needs of a particular publisher. Toillustrate, utilizing the digital campaign modification system apublisher can set multiple triggering conditions that correspond tomultiple actions to automatically modify a content distribution campaigntime according to the unique needs of that particular publisher. In thismanner, the digital campaign modification system may flexibly adapt thedigital content distribution campaign in real time based on detectedsatisfaction of triggering conditions.

Furthermore, the digital campaign modification system can improveefficiency for both publishers and other operators of the digitalcampaign modification system by reducing the amount of user initiatedqueries and modification requests. Indeed, as mentioned above,conventional systems generally require publishers to query performancedata for a particular campaign, analyze the performance data, and thensubmit requests to modify one or more campaign parameters. The digitalcampaign modification system avoids the inefficiency and inconvenienceof these conventional systems by providing (e.g., suggesting utilizing amachine learning model) selectable triggering conditions and actions,monitoring performance of a content distribution campaign, and thenautomatically modifying the content distribution campaign as triggeringconditions are satisfied.

Additionally, the digital campaign modification system can also reducecomputing resources. Indeed, by reducing the number of user-initiatedqueries and modification requests, the digital campaign modificationsystem can reduce the storage and processing power required both bypublisher computing systems and other computing systems utilized toimplement the digital campaign modification system. For instance, thedigital campaign modification system can reduce the computation cost ofopening a communication channel, sending/receiving communications (i.e.,queries and/or modification requests), and responding to thecommunications. Moreover, by utilizing triggering condition identifiedby the publisher, the digital campaign modification system can reducecomputational resources required to monitor various content distributioncampaigns. Indeed, rather than tracking a wide array of differentfeatures of a content distribution campaign in order to respond topublisher queries, the digital campaign modification system can focusresources by monitoring triggering conditions specifically identified byparticular publishers for particular content distribution campaigns.

These advantages are particularly significant in the context ofproviding digital content in a real-time online automated auctionenvironment. Indeed, as discussed below, the digital campaignmodification system 104 can be implemented as part of an onlineautomated auction that provides digital content to user devices withinmilliseconds of detecting an impression opportunity corresponding to theuser devices. Because of the near-instantaneous nature of the real-timeonline automated auction environment, solutions that rely onnon-technical, non-digital solutions are not feasible. The digitalcampaign modification system 104 can increase accuracy, efficiency, andflexibility of content distribution campaigns by modifying contentdistribution campaigns and providing digital content to user devices inaccordance with the content distribution campaign within milliseconds ofidentifying impression opportunities.

As illustrated by the foregoing discussion, the present disclosureutilizes a variety of terms to describe features and advantages of thedigital campaign modification system. Additional detail is now providedregarding the meaning of such terms. For instance, as used herein, theterm “content distribution campaign” refers to a plurality of actions,rules, and/or processes for disseminating one or more content items. Inparticular, a content distribution campaign includes one or more contentitems (e.g., advertisements) and one or more campaign parameters fordisseminating the one or more content items. To illustrate, a digitalcontent distribution campaign includes a content item together withcampaign parameters for bidding on impression opportunities (e.g., abidding schedule, a maximum bid, or a minimum bid), sending contentitems to user devices (e.g., preferred distribution channels), ortargeting particular user devices and/or users (e.g., a target audienceor target user parameters).

Moreover, as used herein, the term “impression,” refers to digitalcontent retrieved from a source (e.g., via a remote server) to be servedfor a particular purpose (e.g., served in a content item slot or spaceto a user device). In particular, an impression corresponds to acountable event for when a content item is retrieved and served (e.g.,regardless of whether a user views or otherwise interacts with thecontent item). Similarly, the term “impression opportunity” refers to anoccasion or prospect to provide an impression to a user device. Forinstance, the term “impression opportunity” includes a vacant digitalcontent slot (e.g., an advertising slot on a social networking newsfeed, website, or application) specific to a user of a user device,where the vacant digital content slot is available to be filled withdigital content from a remote server.

Moreover, as used herein, the term “triggering condition” refers to anevent, activity, incident, or occurrence. In particular, the term“triggering condition” includes an event, activity, incident oroccurrence that initiates performance of an action. As mentioned above,a user interface can include a triggering condition. For example, a userinterface can include a selectable element that reflects an event,activity, incident, or occurrence. To illustrate, a threshold triggeringcondition can include a budget threshold, a cost threshold, or animpressions threshold. In one or more embodiments, triggering conditionsare time-sensitive. For example, a time sensitive triggering conditionmay include a time of day requirement, day of the week requirement, ormonth of the year requirement.

The term “action,” as used herein, refers to a step, performance, orprocess. In particular, the term action includes a step, performance, orprocess initiated in response to satisfaction of a triggering condition.As mentioned, a user interface can include (e.g., portray) an action.For example, a user interface can include a selectable element thatreflects a step, performance, or process to take in response tosatisfaction of a triggering condition. To illustrate, actions mayinclude a modify budget action (e.g., increase or decreasing the totalbudget, or changing budget allocated between two different contentdistribution campaigns), a modify target audience action (e.g.,modifying a target parameter, such as age, for user of client devicestargeted to receive digital content), a modify bid amount action (e.g.,modify a maximum bid or minimum bid or other bid schedule), a modifydigital content action (e.g., change a color of digital content providedto client devices or swap out a first digital content item for a seconddigital content item), or a pause content distribution campaign action.

As used herein, the term “custom rule” includes an instruction or rulecorresponding to at least one content distribution campaign. Inparticular, a custom rule includes one or more triggering conditions andone or more actions corresponding to the one or more triggeringconditions. Thus, the digital campaign modification system can receiveuser interaction via a user interface with a triggering condition and anaction and generate a custom rule from the triggering condition and theaction. In particular, the digital campaign modification system cangenerate a custom rule that includes performing the action upondetecting satisfaction of the triggering condition.

As used herein, the term “machine learning model” refers to a computerrepresentation that can be tuned (e.g., trained) based on inputs toapproximate unknown functions. In particular, the term “machine-learningmodel” can include a model that utilizes algorithms to learn from, andmake predictions on, known data by analyzing the known data to learn togenerate outputs that reflect patterns and attributes of the known data.For instance, a machine-learning model can include but is not limitedto, decision trees, support vector machines, linear regression, logisticregression, Bayesian networks, clustering, K-nearest neighbors, K-means,random forest learning, dimensionality reduction algorithms, boostingalgorithms, artificial neural networks, deep learning, etc. Thus, amachine-learning model makes high-level abstractions in data bygenerating data-driven predictions or decisions from the known inputdata.

Turning now to FIG. 1 , additional detail will be provided regarding anenvironment for implementing a digital campaign modification system.Specifically, FIG. 1 illustrates a schematic diagram of one embodimentof an exemplary system environment (“environment”) 100 in which adigital campaign modification system 104 can operate. As illustrated inFIG. 1 , the environment 100 can include a server 102, a network 112, aplurality of publisher devices 114 a-114 n, and a plurality of userdevices 116 a-116 n. Furthermore, the server 102 includes a digitalcampaign monitoring system 103, which includes the digital campaignmodification system 104. Moreover, the digital campaign modificationsystem 104 also includes a digital content campaign system 106, anonline automated auction system 108, and an online automated billingsystem 110. The server 102, the network 112, the plurality of publisherdevices 114 a-114 n, the plurality of user devices 116 a-116 n, and theother components of the environment 100 may be communicatively coupledwith each other either directly or indirectly (e.g., through the network112).

As just mentioned, and as illustrated in FIG. 1 , the environment 100can include the plurality of publisher devices 114 a-114 n and theplurality of user devices 116 a-116 n. The plurality of publisherdevices 114 a-114 n and the plurality of user devices 116 a-116 n maycomprise a variety of different computing devices, such as personalcomputers, laptop computers, mobile devices, smartphones, tablets,special purpose computers, TVs, or other computing devices. Asillustrated in FIG. 1 , the plurality of publisher devices 114 a-114 n,the plurality of user devices 116 a-116 n, and/or the server 102 maycommunicate via the network 112. Additional detail regarding theplurality of publisher devices 114 a-114 n, plurality of user devices116 a-116 n, and the network 112 are discussed in greater detail belowin relation to FIG. 11 .

Moreover, as illustrated by FIG. 1 , the environment 100 also includesthe server 102. The server 102 may generate, store, receive, and/ortransmit any type of data. The server 102 can comprise a communicationserver or a web-hosting server. In one or more embodiments, the server102 may comprise a data server. Additional details regarding the server102 will be discussed below in relation to FIG. 11 .

As illustrated, in one example, the server 102 can host the digitalcampaign monitoring system 103. The digital campaign monitoring system103 can manage the monitoring of a digital content distributioncampaign, and schedule the implementation of custom rules applicable tothe digital content distribution campaign. For instance, the digitalcampaign monitoring system 103 can monitor bids, winning bids, spend,billing, user interactions with digital content, or other activitiescorresponding to a digital content distribution campaign.

Moreover, the server 102 can host the digital campaign modificationsystem 104. As mentioned above, the digital campaign modification system104 can dynamically modify a content distribution campaign based on oneor more triggering conditions and actions. The digital campaignmodification system 104, as further illustrated in FIG. 1 , can includethe digital content campaign system 106, the online automated auctionsystem 108, and the online automated billing system 110. As an overview,the digital content campaign system 106 manages content distributioncampaigns as well as serves content items to user devices. The onlineautomated auction system 108 facilitates online auctions betweendifferent publishers and/or different content distribution campaigns.Moreover, the online automated billing system 110 collects actualrevenue attributable to (e.g., billed to or able to be billed to) apublisher (e.g., an advertiser).

While FIG. 1 shows the digital campaign monitoring system 103, thedigital campaign modification system 104, the digital content campaignsystem 106, the online automated auction system 108, and the onlineautomated billing system 110 located on the server 102, each of thesesystems can be implemented on the same or different servers. Forexample, in one or more embodiments, the digital campaign monitoringsystem 103 is implemented via a first set of servers, the digitalcampaign modification system 104 is implemented via a second set ofservers, the online automated auction system 108 is implemented by athird set of servers, and/or the online automated billing system 110 isimplemented via a fourth set of servers.

Moreover, although FIG. 1 illustrates the digital campaign monitoringsystem 103 implementing the digital campaign modification system 104,and the digital campaign modification system 104 implementing thedigital content campaign system 106, the online automated auction system108, and the online automated billing system 110, in some embodiments,different arrangements are utilized. For example, in one or moreembodiments, the digital campaign monitoring system 103 can beimplemented (in whole or in part) within one or more of the digitalcampaign modification system 104, the digital content campaign system106, the online automated auction system 108, and the online automatedbilling system 110. For example, in one or more embodiments, the digitalcampaign monitoring system 103 is implemented (e.g., hosted) as part ofthe digital campaign modification system 104. Similarly, the digitalcampaign modification system 104 can be implemented (in whole or inpart) within one or more of the digital content campaign system 106, theonline automated auction system 108, and the online automated billingsystem 110.

Although FIG. 1 illustrates one of the server 102, it will beappreciated that the server 102 can represent any number of computingdevices. Similarly, although FIG. 1 illustrates a particular arrangementof the server 102, network 112, plurality of publisher devices 114 a-114n, and plurality of user devices 116 a-116 n, various additionalarrangements are possible.

By way of example, in one or more embodiments the publisher device 114 asends a request to the server 102 to generate a custom rule as part of acontent distribution campaign. The digital campaign modification system104, via the server 102, can provide, to the publisher device 114 a, auser interface with triggering conditions and actions for generating acustom rule, in addition to other campaign parameters for a contentdistribution campaign. In particular, the server 102 can utilize amachine learning model to generate suggested triggering conditionsand/or actions and provide the suggested triggering conditions and/oractions for display via the publisher device 114 a. The publisher device114 a can detect user interaction with triggering conditions and/oractions and send the publisher input to the server 102. In response, thedigital campaign modification system 104, via the server 102, cangenerate a custom rule associated with a digital content campaign forthe publisher. Moreover, the digital campaign modification system 104can utilize the digital content campaign system 106 to execute thedigital content campaign for the publisher.

In implementing the digital content campaign, the digital campaignmodification system 104 can monitor impression opportunities at varioususer devices and provide digital content to the various user devices inaccordance with the publisher campaign parameters. For example, the userdevice 116 a can send a request to the server 102 (e.g., via a socialnetworking application) for digital content. The digital contentcampaign system 106 can identify an impression opportunity correspondingto the user device 116 a (e.g., an advertising slot within a socialnetworking feed of the social networking application). In response, thedigital content campaign system 106 can provide information regardingthe impression opportunity (e.g., user characteristics and otherinformation regarding the advertising slot) to the online automatedauction system 108. The online automated auction system 108 candetermine bids for the impression opportunity corresponding to the userdevice 116 a based on campaign parameters provided by variouspublishers. The online automated auction system 108 can furtherdetermine a winning bid and winning publisher for the impressionopportunity. The digital content campaign system 106 can then providedigital content corresponding to the winning publisher to the userdevice 116 a. Furthermore, the digital campaign modification system 104can monitor user interaction at the user device 116 a with the digitalcontent and provide information to the online automated billing system110 to determine a publisher to bill.

As mentioned above, the digital campaign modification system 104 cancontinuously monitor execution of a digital content campaign over timeand determine when one or more triggering conditions have beensatisfied. For example, the digital campaign modification system 104 candetermine that a triggering condition received from the publisher device114 a has been satisfied (e.g., a particular budget amount has beenspent) and, in response, perform an action corresponding to thetriggering condition. For example, the digital campaign modificationsystem 104 can lower a bid amount for utilization with the contentdistribution campaign via the online automated auction system 108.Moreover, the digital campaign modification system 104 can then executethe modified content distribution campaign.

In one or more embodiments, the digital campaign modification system 104can continuously monitor execution of a digital content campaign butlimit the number and/or rate of modifications applied to a digitalcontent campaign. For example, in one or more embodiments, the digitalcampaign modification system 104 applies a rule application threshold tolimit the number (or frequency) of actions and modifications applied toa digital content campaign. Specifically, the digital campaignmodification system 104 can apply a rule application threshold thatlimits the number of actions/rules that can be automatically appliedwithin a particular unit of time. More specifically, the digitalcampaign modification system 104 can verify if the rule applicationthreshold rate has been reached prior to automatically performing thecorresponding action. For example, the digital campaign modificationsystem 104 can apply a rule application threshold that limits thedigital campaign modification system 104 to one action per five minutes(and thus limit modifications to one action within a five minuteinterval, even if the digital campaign modification system 104 detectsadditional triggering conditions).

As illustrated by the previous example embodiment, the digital campaignmodification system 104 may be implemented in whole, or in part, by theindividual elements 102-116 n of the environment 100. Although FIG. 1illustrates the digital campaign modification system 104 implementedwith regard to the server 102, it will be appreciated that components ofthe digital campaign modification system 104 can be implemented in anyof the components of the environment 100. For example, in one or moreembodiments, the digital campaign modification system 104 can beimplemented at least in part on the publisher device 114 a.

Turning now to FIGS. 2A-2B, additional detail will be provided regardingdynamically modifying a content distribution campaign in accordance withone or more embodiments of the digital campaign modification system 104.In particular, FIGS. 2A-2B illustrate a sequence of acts 202-230performed by the digital campaign modification system 104 for generatingand applying custom rules corresponding to a digital content campaign.Although FIGS. 2A-2B show the digital campaign modification system 104residing on the server 102, the digital campaign modification system 104can reside in part on the publisher device 114 a, the server 102, andthe user device 116 a to perform the acts 202-230. In particular, asdiscussed in more detail below, the digital campaign modification system104 can comprise computer-executable instructions that, when executed bythe publisher device 114 a, the server 102, and/or the user device 116a, cause the publisher device 114 a, the server 102, and/or the userdevice 116 a to perform the acts 202-230 shown in the sequence diagramof FIGS. 2A-2B.

As illustrated in FIG. 2A, the digital campaign modification system 104can perform the act 202 of receiving digital campaign parameters.Specifically, the act 202 can include sending data associated with adigital content distribution campaign from the publisher device 114 a tothe server 102. For example, the server 102 can receive campaignparameters that include digital content (e.g., advertisements), a targetaudience (e.g., targeting parameters for particular users), a budget,bidding schedule, distribution avenues (e.g., preferred avenues fordistribution of digital content, such as preferred websites orapplications), a campaign duration, and goals. The act 202 can alsoinclude receiving information regarding the publisher, such as businesssize, industry, products or services offered, revenue, etc.

Upon receiving the digital campaign parameters, the digital campaignmodification system 104 performs the act 204 of analyzing the digitalcampaign parameters (and/or other information regarding the publisher).In particular, the act 204 can include utilizing a machine learningmodel to analyze the digital campaign parameters and generatesuggestions for the digital content distribution campaign. For example,the act 204 can include accessing a machine learning model trainedutilizing a plurality of (historical) content distribution campaigns andcorresponding content distribution campaign performance data.Furthermore, the act 204 can include utilizing the trained machinelearning model to generate suggested triggering conditions and/orsuggested actions. Additional detail regarding training and utilizing amachine learning model to generate suggestions is provided below inrelation to FIGS. 7-8 .

As illustrated in FIG. 2A, the digital campaign modification system 104also performs the act 206 of providing the digital content distributioncampaign suggestion to the publisher device 114 a. For instance, the act206 can include sending data associated with the digital campaignparameters received at the act 202, to the publisher device 114 a. Inparticular, the act 206 can include providing the suggestions fordisplay to the publisher device 114 a as part of a user interface.

For example, as shown in FIG. 2A, the digital campaign modificationsystem 104 can perform the act 206 as part of performing the act 208 ofsending a user interface with triggering conditions and actions to thepublisher device 114 a. In relation to the act 208, the server 102 canprovide an interface with a variety of interactive elements to enable apublisher to select triggering conditions and/or actions for a customrule. For instance, the act 208 can include providing a user interfacethat includes a plurality of selectable triggering condition elementsand selectable action elements. Indeed, the user interface can includeelements for adding or removing triggering conditions, actions,notifications, and/or a rate of monitoring.

As illustrated in FIG. 2A, after the digital campaign modificationsystem 104 sends the user interface (e.g., with triggering conditionsand actions) to the publisher device 114 a, the digital campaignmodification system 104 utilizes the publisher device 114 a to performthe act 210 of receiving interaction with the user interface.Specifically, the publisher device 114 a can receive user input from thepublisher via the user interface. For example, the publisher device 114a can receive user interaction with (e.g., user selection of) triggeringconditions and actions portrayed in the user interface.

After receiving the interaction with the user interface at the act 210,the digital campaign modification system 104 can perform act 212 ofsending the selected triggering conditions and actions (e.g., anindication of the selected triggering conditions and actions) from thepublisher device 114 a to the server 102. Moreover, upon receiving anindication of the selected triggering conditions and actions, thedigital campaign modification system 104 performs the act 214 ofgenerating a custom rule. Specifically, the digital campaignmodification system 104 utilizes at least the triggering conditions andactions received at the act 212 to generate the custom rule.

To provide a specific example, in one or more embodiments, the publisherdevice 114 a selects a triggering condition of click-through-ratedropping below 0.25 and an action of stopping delivery of digitalcontent (e.g., a publisher may be uncertain how digital content willperform and wants to stop delivering the digital content when theclick-through-rate drops below 0.25). The publisher device 114 a canprovide the triggering condition and the action to the server 102, andthe digital campaign modification system 104 couples the selectedtriggering condition with its corresponding selected action to generatea custom rule: stopping delivery of the digital content when theclick-through-rate drops below 0.25. In this manner, the digitalcampaign modification system 104 can combine the selected triggeringconditions and actions into an executable rule associated with at leastone digital content distribution campaign.

Furthermore, the act 214 can also include generating a custom rule for aplurality of triggering conditions and actions. For example, in one ormore embodiments, the custom rule can include a plurality of triggeringconditions, wherein the individual triggering conditions differ. Forinstance, two triggering conditions associated with the custom rule mayeach relate to when the cost per impression opportunity reaches acertain threshold, but differ in the magnitude of the threshold (e.g.,when the cost is greater than $0.50, as compared to when cost is greaterthan $1.00). In this manner, the triggering conditions may staggercorrelated actions automatically with gradual increases or decreases.

As just mentioned, performing the act 214 can include generating thecustom rule associated with a plurality of actions. Indeed, a customrule can include two different actions for one or more triggeringevents. To illustrate, where the cost exceeds a particular threshold,the custom rule can include a first action for modifying a maximum bidamount and a second action for modifying a budget amount.

While the foregoing examples illustrate custom rules with the triggeringconditions and actions, the custom rule may also include additionalelements. For instance, the custom rule may also include a rate ofmonitoring and/or notifications as described above.

As illustrated in FIG. 2A, the digital campaign modification system 104also performs the act 216 of executing a content distribution campaign.The act 216 can include a variety of processes and acts for deliveringdigital content of the digital content distribution campaign to a targetaudience. For example, as discussed above, the act 216 can includemonitoring user devices for impression opportunities and then conductingan online automated auction. In particular, the act 216 can compriseapplying campaign parameters for the digital content campaign in anonline automated auction to generate bids for the publisher.Furthermore, the act 216 can include identifying a winning bid andwinning publisher from the online automated auction and identifyingdigital content from the winning publisher. Accordingly, the act 216 caninclude repeatedly analyzing impression opportunities, generating bidsaccording to campaign parameters of the content distribution campaign,and then identifying digital content according to the campaignparameters of the digital content campaign.

As shown in FIG. 2A, the digital campaign modification system 104 alsoperforms the act 218 of delivering the digital content to the userdevice 116 a. In particular, the server 102 can deliver the digitalcontent according to the campaign parameters received from the publisherdevice 114 a. For example, the server 102 can identify an impressionopportunity associated with the user device 116 a, execute an onlineautomated auction for the impression opportunity, identify the publisherassociated with the publisher device 114 a as a winning bidder, selectdigital content (e.g., an advertisement) to provide to the user device116 a according to the campaign parameters provided via the publisherdevice 114 a, and then provide the digital content to the user device116 a. Although the foregoing examples refers to only one user device116 a, the act 218 can also include delivery of various digital contentitems to the plurality of user devices 116 a-116 n.

As illustrated in FIG. 2B, after the digital campaign modificationsystem 104 sends the digital content, the user device 116 a performs theact 220 of receiving interaction with the digital content. For example,the user may interact with an application running on the user device 116a (e.g., a networking application) to select (e.g., click) the digitalcontent, visit a website corresponding to the digital content, orpurchase an item corresponding to the digital content. The digitalcampaign modification system 104 can identify these user interactions atthe user device 116 a. Moreover, as shown, the digital campaignmodification system 104 can perform the act 222 of providing anindication of the interaction with the digital content to the server102.

While only one user device 116 a is shown in FIG. 2B, the digitalcampaign modification system 104 can provide digital content andidentify user interactions with the plurality of user devices 116 a-116n. For example, the plurality of user devices 116 a-116 n may eachreceive digital content designated for delivery to each device and eachdevice may perform the act 220 of receiving interaction with the digitalcontent. Then, the plurality of user devices 116 a-116 n can eachperform the act 222 of sending the interaction with the digital contentto the server 102.

As shown in FIG. 2B, after performing the act 222, the digital campaignmodification system 104 performs the act 224 of detecting satisfactionof a triggering condition. Specifically, the act 224 includes monitoringprogress of a content distribution campaign (e.g., monitoring number ofwinning bids, budget allocation, and/or user interactions with digitalcontent) and detecting satisfaction of a triggering condition in thecustom rule associated with the digital content distribution campaign.For instance, the custom rule generated at the act 214 can include atriggering condition setting when the cost exceeds a threshold value. Inthis case, if the cost at an online automated auction at the server 102exceeds the threshold value, then the digital campaign modificationsystem 104 will detect satisfaction of the triggering condition.Similarly, the custom rule generated at the act 214 can include athreshold number of user interactions with digital content. The digitalcampaign modification system 104 can detect (e.g., from the interactionsreceived at the act 222) satisfaction of the triggering condition.

As shown in FIG. 2B, after performing the act 224, the digital campaignmodification system 104 performs the act 226 of modifying the contentdistribution campaign. In particular, the digital campaign modificationsystem 104 applies the actions correlated with the triggering conditionsas defined in the custom rule. For example, the custom rule may includea targeting condition of the daily budget spent exceeding $30 and acorresponding action of decreasing maximum bids for an online automatedauction to $0.75. In response to detecting that the daily budget spentexceeds $30, the digital campaign modification system 104 canautomatically decreases the maximum bids to $0.75.

As discussed above, the digital campaign modification system 104 canalso apply a rule application threshold in performing the act 224. Forexample, the digital campaign modification system 104 can determinewhether a rule application threshold has been satisfied before modifyingthe content distribution campaign. For instance, continuing theforegoing example, the digital campaign modification system 104 may waitto perform (or ignore) the action of automatically decreasing themaximum bids to $0.75 upon determining that a rule application thresholdrate has already been exceeded (i.e., there has already been an actionperformed in response to a triggering condition in the preceding fiveminutes).

As mentioned above, in one or more embodiments, the custom rule may alsoinclude notification(s) settings. If indicated, the notification(s)settings in the custom rule may instruct the digital campaignmodification system 104 to deliver a notification to the publisher. Thenotification may include a message indicating that the automaticmodification of the digital content distribution campaign occurred. Forexample, the digital campaign modification system 104 may deliver to thepublisher device 114 a a message notification indicating that themaximum bid was reduced to $0.75.

As illustrated in FIG. 2B, the digital campaign modification system 104performs act 230 of executing the modified content distributioncampaign. For example, continuing the previous example, the digitalcampaign modification system 104 can execute the modified contentdistribution campaign by identifying an impression opportunity andlimiting a maximum bid amount at an online auction to $0.75. Asmentioned above, executing the modified content distribution campaigncan include performing a variety of different processes, rules, or stepsin accordance with a particular action selected by a publisher.

The method described in relation to FIGS. 2A-2B is intended to beillustrative of one or more methods in accordance with the presentdisclosure, and is not intended to limit potential embodiments.Alternative embodiments can include additional, fewer, or different actsthan those articulated in FIGS. 2A-2B. For instance, as shown in FIG.2A, in some embodiments, the acts 202-206 are optional (e.g., are notperformed). For instance, in some circumstances the digital campaignmodification system 104 performs the act 208 by including suggestedtriggering conditions and/or actions for a publisher to utilize. Inother embodiments, the digital campaign modification system 104 providesa user interface (e.g., a general list of possible triggering conditionsand/or actions) without any explicit suggestions.

Additionally, the acts described herein may be performed in a differentorder, may be repeated or performed in parallel with one another, or maybe performed in parallel with different instances of the same or similaracts. For example, while the illustration in FIG. 2A shows the act 216occurring after the act 214, the acts may occur in an alternative order.For example, when the digital campaign modification system 104previously executed the digital content distribution campaign, andsubsequently makes a custom rule. In this instance, the digital campaignmodification system 104 provides the subsequently made custom rule forimplementation in association with the previously executed digitalcontent distribution campaign. In this manner, performing the act 216can include executing the content distribution campaign, now associatedwith the custom rule generated at the act 214.

Turning now to FIGS. 3A-3D, additional detail will be provided regardinguser interfaces for designing digital content campaigns and settingtriggering conditions and actions in accordance with one or moreembodiments of the digital campaign modification system 104. Inparticular, FIGS. 3A, 3B illustrate user interfaces for designing andreviewing content distribution campaigns that include a selectableelement for generating a custom rule. FIGS. 3C, 3D illustrates a userinterface (e.g., upon user interaction with the selectable element forgenerating a custom rule) for selecting triggering conditions andactions.

For example, FIG. 3A illustrates a publisher device 300 (e.g., anexemplary embodiment of the publisher device 114 a) running anapplication 302 that includes a user interface 304. In one or moreembodiments, the application 302 allows a publisher to create contentdistribution campaigns (e.g., advertising campaigns) with correspondingcampaign parameters. Indeed, as illustrated, the user interface 304includes tabs for generating “Campaigns,” “Ad Sets,” and “Ads.” Each ofthe tabs include elements for generating campaigns, ad sets, and ads(i.e., digital content). Thus, in relation to the tab “Ad Sets” the userinterface 304 includes an element to “Create Ad Set” and selectableelements to modify an existing ad set (e.g., the ad set for “Florida”).

Notably the user interface 304 also includes the custom rule element306. In particular, the custom rule element 306 is labeled “CreateRule.” Upon user interaction with the custom rule element 306, thedigital campaign modification system 104 can provide one or moreadditional selectable elements and/or user interfaces for generating acustom rule with triggering conditions and/or actions. For example, uponuser interaction with the custom rule element 306, the digital campaignmodification system 104 can provide a drop-down menu with triggeringconditions and actions (e.g., suggested triggering conditions and/oractions for a particular ad set and/or campaign).

Similarly, FIG. 3B illustrates the publisher device 300 running anapplication 312 that includes a user interface 314. In one or moreembodiments, the application 312 allows a publisher to analyzeperformance of a content distribution campaign. For example, the userinterface 314 includes various elements (e.g., graphs, tables, andcharts) reflecting various performance metrics relative to a contentdistribution campaign.

The user interface 314 also includes the custom rule element 306. Asmentioned above, upon user interaction with the custom rule element 306,the digital campaign modification system 104 can provide additionalselectable elements and/or user interfaces for generating a custom rule.Thus, as shown in FIGS. 3A, 3B, the digital campaign modification system104 can provide elements for generating a custom rule in a variety ofdifferent applications, including applications for generating oranalyzing content distribution campaigns.

As mentioned above, the digital campaign modification system 104 canalso generate a user interface comprising triggering conditions andrules. For example, FIG. 3C illustrates the publisher device 300portraying a user interface 320. In one or more embodiments, the digitalcampaign modification system 104 provides the user interface 320 upondetecting user interaction with the custom rule element 306.Accordingly, the user interface 320 can be provided as part of theapplication 302, the application 312, and/or some other application.

As illustrated in FIG. 3C, the user interface 320 includes a variety ofselectable elements for generating a custom rule. In particular, theuser interface 320 includes a selectable element 322 for adding contentdistribution campaigns or portions of content distribution campaignsapplicable to a custom rule (in addition to a drop-down element forselecting applicable content distribution campaigns or portions ofcontent distribution campaigns). The user interface 320 also includes aselectable element 324 for adding and/or selecting actions, a selectableelement 326 for adding and/or selecting triggering conditions, aselectable element 328 for adding and/or selecting a frequency (i.e., arate of monitoring for satisfaction of triggering conditions), and aselectable element 330 for adding and/or selecting a notification. Theuser interface 320 also includes fields for identifying informationassociated with a custom rule, including a subscriber 332 and rule name334. Moreover, the user interface 320 also includes administrativeelements to create 336 or cancel 338 the creation of the custom rule.

As indicated above, in one or more embodiments, the user interface 320includes the selectable element 322 for adding content distributioncampaigns (or portions of content distribution campaigns) applicable toa custom rule. Indeed, as mentioned above, the digital campaignmodification system 104 can apply a custom rule to multiple differentcontent distribution campaigns or multiple parts of content distributioncampaigns. For example, the digital campaign modification system 104 canapply a custom rule to a plurality of digital content items (e.g.,advertising sets) in a content distribution campaign or a single digitalcontent item (e.g., a single advertisement) in a content distributioncampaign. Based on user interaction with the selectable element 322, thedigital campaign modification system 104 can adjust the applicability ofa custom rule to all or a portion of content distribution campaigns.

Thus, in response to user interaction with the selectable element 322,the digital campaign modification system 104 can provide a plurality ofdigital content campaigns for display (e.g., a list of available digitalcontent campaigns) or provide a plurality of different portions of adigital content campaign for display (e.g., a list of different digitalcontent items). The publisher can select one or more digital contentcampaigns. In response, the digital campaign modification system 104 canapply a custom rule to the one or more selected digital contentcampaigns (e.g., monitor the one or more selected digital contentcampaigns for triggering conditions and apply actions to the one or moreselected digital content campaigns upon satisfaction of the triggeringconditions).

Similarly, user interaction with selectable elements 322, and 326 allowsa publisher to add actions and corresponding triggering conditions to aparticular custom rule. For example, a publisher can interact with theselectable element 322 and add an action to increase a daily budget orincrease a per impression budget. Similarly, a publisher can interactwith the selectable element 326 and add a triggering conditioncorresponding to the action, such as detecting that lifetime impressionsexceed a particular number or that an amount of budget spent exceeds aparticular value. Thus, a publisher can add multiple actions andcorresponding triggering conditions to a particular custom rule viainteraction with the selectable elements 322, and 326.

As illustrated in FIG. 3C, the user interface 320 can also includeelements for additional details regarding a triggering condition. Forexample, the user interface 320 can include a selectable element for atime range applicable to the triggering condition (e.g., within the lastmonth or year). Similarly, the user interface can include a selectableelement for a particular approach to attribution (e.g., a method orapproach to attributing conversions or events to a digital content itemor digital content campaign such that monitoring for the triggeringcondition is performed according to the publisher's preferences).

Utilizing the added actions and corresponding triggering conditions, thedigital campaign modification system 104 also defines a custom rule. Forexample, the digital campaign modification system 104 defines what datato monitor according to the added triggering conditions. Similarly, thedigital campaign modification system 104 defines what action to takeupon detection of satisfaction of the added triggering condition byreferring to the corresponding added actions.

As shown in FIG. 3C, the user interface 320 also includes a selectableelement 328 for adding and/or selecting a frequency. Based on userinteraction with the selectable element 320 the digital campaignmodification system 104 can adjust a monitoring frequency to aparticular custom rule. For example, a publisher can add a monitoringfrequency of Daily at 12:00 AM Pacific Time to the particular customrule. In this manner, the publisher may instruct the digital campaignmodification system 104 to check for satisfaction of any addedtriggering conditions at a particular frequency (i.e., rate ofmonitoring) for the particular custom rule.

As shown in FIG. 3C, the user interface 320 also includes a selectableelement 330 for adding a notification. Based on user interaction withthe selectable element 330, the digital campaign modification system 104can add a notification for a particular custom rule via the selectableelement 330. Specifically, the publisher can indicate a preferred methodof receiving a notification from the digital campaign modificationsystem 104 when the triggering conditions are satisfied. By way ofexample, the added notification instruction can direct the digitalcampaign modification system 104 to send a notification to the publishervia a networking system.

As just mentioned, a publisher can interact with the user interface 320to select actions, triggering conditions, and other features. FIG. 3Dshows user interface 320 upon receiving various selections via thecomputing device 300. Specifically, FIG. 3D illustrates publisherselections via the elements 322-334.

For example, FIG. 3D illustrates publisher selections made via theselectable element 322. Specifically, the user interface 320 illustratesan ad set 340 (entitled “Ad Set 1”). Based on the selected ad set 340,the digital campaign modification system 104 applies the actions,triggering conditions, and other features selected via the userinterface 320 to Ad Set 1. The digital video insertion system 104 canadd one or more additional digital content campaigns (or portions ofdigital content campaigns) based on additional user interaction. In thismanner, a publisher can manage associations between a plurality ofcontent distribution campaigns and a particular custom rule.

FIG. 3D also illustrates publisher selections made via the selectableelement 324 for adding and/or selecting an action. Specifically, theuser interface 320 illustrates an action 342 (which includes an actiontype, “Increase Daily Budget By,” as well as an action quantity,“$11.00”). When adding an action type, a publisher may select from aplurality of selectable actions. If the action type selected requires aquantity, a publisher can use the field entry to provide a quantityassociated with the type of action (e.g., a dollar figure, amount, orpercentage).

In response to selection of different action types, the digital campaignmodification system 104 can add selectable customization options withinthe user interface 320. For example, in response to selection of the“Increase Daily Budget By” action type, the digital campaignmodification system 104 includes the max daily budget cap element 344.When the max daily budget cap 344 is available, a publisher can use thefield entry location to supply a quantity for capping the max dailybudget. After the publisher indicates the selected actions, the digitalcampaign modification system 104 will utilize the selected actions ingenerating the custom rule.

FIG. 3D also illustrates a publisher selection made via the selectableelement 326 for adding and/or removing triggering conditions. Forexample, the user interface 320 includes the triggering condition 346(i.e., “Lifetime Impressions >8000”) and the second triggering condition348 (i.e., “Daily Spend >$30”). The triggering condition 346 causes thedigital campaign modification system 104 to monitor for when lifetimeimpressions for the content distribution campaign associated with thecustom rule is greater than 8000. The second triggering condition 348causes the digital campaign modification system 104 to monitor thecontent distribution campaign associated with the custom rule for whenthe daily dollar figure spent is greater than $30.00.

As shown, triggering conditions can include elements for both atriggering condition type and a triggering condition quantity (e.g., atriggering condition type of “Lifetime Impressions” and a triggeringcondition quantity of “8000”). In some embodiments, the publisherselects the triggering condition type from a plurality of selectabletriggering condition types provided via the digital campaignmodification system 104 (e.g., cost per mobile app install, percent oftotal budget spent, or daily clicks). In one or more embodiments, thepublisher enters a quantity in a field entry location (e.g., a dollarfigure, amount, or percentage). In this manner, the publisher can selecttriggering conditions that the custom rule will monitor.

When the publisher selects more than one triggering condition, the userinterface 320 can provide additional options to the publisher. Forexample, the digital campaign modification system 104 can provideadditional options defining a relationship between triggeringconditions. To illustrate, the digital campaign modification system 104can provide logical connectors (e.g., Boolean connectors), or sequenceconnectors (i.e., an ordering of priority). For example, the publishercan indicate that trigger condition 346 is logically connected with the“AND” requirement to the second triggering condition 348. Thus, thepublisher can indicate that the custom rule will only be satisfied whenboth individual triggering conditions are satisfied.

The triggering conditions further comprise an option to indicate thetime range 350. Specifically, the time range 350 can indicate the timespan over which the digital campaign modification system 104 willcalculate the triggering conditions (e.g., the monitored amounts of thecontent distribution campaign associated with the custom rule). By wayof example, the publisher can select the time range of beginning on Jan.1, 2017. With this selected time range, the digital campaignmodification system 104 will calculate any values correlated to thecontent distribution campaign associated with the custom rule from thatdate up to the present. The time range 350 can comprise of a pluralityof time ranges (e.g., varying start and stop dates, or discrete spans oftime, such as 3 months).

The digital campaign modification system 104 further manages thetriggering conditions via the attribution window 352. As brieflymentioned above, via the attribution window 352 the digital campaignmodification system 104 can manage attribution for activity related tothe content distribution campaign associated with the custom rule. Forinstance, the attribution window 352 can include an option to attributeinteraction with digital content of the content distribution campaignassociated with the custom rule if the interaction was one day after auser viewing the digital content, or twenty-eight days after interactingwith the digital content (e.g., clicking on an advertisement). Byidentifying how the digital campaign modification system 104 willcalculate attribution of interaction with digital content, the digitalcampaign modification system 104 is able to accurately monitor activityand detect triggering conditions.

FIG. 3D also illustrates the elements to create 336, or cancel 338 thecreation of the custom rule. After the digital campaign modificationsystem 104 creates the custom rule, it can utilize and apply the customrule. For example, utilizing this and other custom rules (e.g.,historical content distribution campaign data/historical custom ruledata), the digital campaign modification system 104 can execute one ormore digital content campaigns. Moreover, the digital campaignmodification system 104 can utilize the digital content campaign andcustom rules to generate suggestions for other publishers.

Indeed, as discussed above, in one or more embodiments, the digitalcampaign modification system 104 generates suggestions for a publisherin relation to a digital content campaign. For example, FIG. 4illustrates a user interface including suggested targetingcharacteristics and actions in accordance with one or more embodimentsof the digital campaign modification system 104. Specifically, FIG. 4illustrates the publisher device 300 with the user interface 320. Thedigital campaign modification system 104 generates and provides fordisplay via the user interface 320 a plurality of suggestions, includinga suggested action 402 and a suggested triggering condition 406.

As mentioned above, in one or more embodiments, the digital campaignmodification system 104 can generate suggestions utilizing a machinelearning model. In relation to FIG. 4 , the digital campaignmodification system 104 generates the suggested action 402 and thesuggested triggering condition 406. For example, the digital campaignmodification system 104 can identify features of one or more digitalcontent campaigns of the publisher and analyze the features utilizing amachine learning model. The machine learning model can then suggesttriggering conditions and/or actions for the digital content campaign.

For instance, in relation to FIG. 4 , the digital campaign modificationsystem 104 identifies a set of digital content items (e.g., the “Ad Set1”) utilized by a publisher as part of a digital content campaign. Thedigital campaign modification system 104 applies a machine learningmodel to various parameters applicable to the set of digital contentitems (e.g., campaign parameters of the digital content campaign). Themachine learning model then generates suggested triggering conditionsand corresponding actions (i.e., the suggested triggering condition 406and the suggested action 402). Moreover, the digital campaignmodification system 104 then provides the suggested triggering condition406 and the suggested action 402 for display via the user interface 320.Additional details concerning generating and applying a machine learningmodel are provided in relation to FIGS. 7-8 .

As just mentioned, the user interface 320 can include the suggestedaction 402. For example, the suggested action 402 may include asuggested indication of what to modify in a digital content campaign,and a suggested indication of the quantity to modify. As shown, forexample, the suggested action 402 includes an “increase Per Click BudgetBy” action and a quantity of “$0.10.” The publisher can accept and/orreject the suggested action 402 (e.g., by selecting the suggested action402).

Additionally, the user interface 320 includes an additional actionsuggestion element 404. In response to user interaction with theadditional action suggestion 404, the digital campaign modificationsystem 104 provide additional action suggestions for display to thepublisher.

The user interface 320 in FIG. 4 also shows a suggested triggeringcondition 406. Similar to the suggested action 402, the suggestedtriggering condition 406 includes a triggering condition type andquantity (i.e., “Daily Clicks” and “100). The publisher may select thesuggested triggering condition 406 to add it to the plurality oftriggering conditions associated with the custom rule. As alsoillustrated in FIG. 4 , the user interface 320 can include an additionaltriggering condition suggestion element 408.

Although not illustrated in FIG. 4 , the digital campaign modificationsystem 104 can also generate other suggestions. For example, the digitalcampaign modification system 104 can suggest notifications and/orfrequencies. For example, a frequency suggestion can include a suggestedrate of monitoring (e.g., “hourly on weekdays Pacific Time”).Additionally, a notification suggestion can include a suggestednotification option (e.g., “On Facebook when all triggering conditionsare met”).

The digital campaign modification system 104 can present suggestions tothe publisher via the user interface 320 at a variety of different times(e.g., in response to a variety of events). For example, the digitalcampaign modification system 104 can present suggestions in response topublisher selection of elements on the user interface 304, the userinterface 314, or the user interface 320. For example, when thepublisher interacts with the custom rule element 306 via the userinterface 304, or the user interface 314, the digital campaignmodification system 104 can provide the user interface 320 with thesuggestions. Moreover, in one or more embodiments, the digital campaignmodification system 104 provides the suggestions to the publisher inresponse to the publisher selecting elements via the user interface 320(e.g., interaction with the selectable element 322). More specifically,the digital campaign modification system 104 can provide the suggestionin response to the user selecting a recommended rule element, atriggering condition and/or action, or in response to some other event.

In addition to suggestions for individual triggering conditions and/oractions, the digital campaign modification system 104 can provide ruleset recommendations. For example, in one or more embodiments, thedigital campaign modification system 104 can generate a user interfacefor managing a plurality of digital content campaigns (or portions ofdigital content campaigns) and provide suggested custom rules applicableto the digital content campaigns (e.g., suggested custom rules that arenew or existing for a particular publisher and/or digital contentcampaign)

Indeed, in one or more embodiments, the digital campaign modificationsystem 104 utilizes a trained machine learning model to analyze acontent distribution campaign data and generate a suggested rule.Moreover, the digital campaign modification system 104 analyzes existingcustom rules to identify any existing custom rules that correlate to thesuggested rule. The existing custom rules that correlate to thesuggested rule are then provided to the publisher.

For example, FIG. 5 shows the publisher device 300 running theapplication 302 corresponding to a user interface 500. As shown, theuser interface 500 includes a campaign list 502, associated rules 504, asuggested rule 508, and an additional rule recommendation element 509.In addition, the user interface 500 includes an option to add rule sets506, and an option to create a new campaign 510.

As just mentioned, the user interface 500 may include a campaign list502. In one or more embodiments, the campaign list 502 displays a listof the active campaigns associated with the publisher. In associationwith the campaign list, the user interface may display the associatedrules 504. Specifically, the associated rules 504 reflect the rules thedigital campaign modification system 104 is applying to thecorresponding campaign on the campaign list 502.

Furthermore, the user interface 500 includes the suggested rules 508.The suggested rules 508 include at least one rule set recommended forassociation with the indicated campaign. The digital campaignmodification system 104 may determine the suggested rule 508 fromexisting rules applicable to one or more digital content campaigns or asa new rule (as mentioned above). Accordingly, as shown, the digitalcampaign modification system 104 can provide a user interface formanaging a plurality of content distribution campaigns and correspondingrules simultaneously, as well as providing suggested rules for one ormore of the plurality of content distribution campaigns.

As mentioned above, the digital campaign modification system 104 canalso provide one or more notifications to a publisher. Specifically,FIG. 6 illustrates a publisher device 600 receiving a notification 604via the device display 602. For example, the publisher device 600 is oneexemplary embodiment of the publisher device 114 b. The digital campaignmodification system 104 sends the notification 604 to the publisherdevice 600 based on monitoring a digital content campaign and detectinga triggering event. Specifically, in response to a triggering event, thedigital campaign modification system 104 determines a notificationcorresponding to the triggering event and provides the notification 604(e.g., as part of the act 228 discussed in relation to FIG. 2 ).

As shown in FIG. 6 , the notification 604 is a notification deliveredvia a networking system (e.g., a social networking system or messagingsystem). Specifically, the notification is delivered via a networkingsystem application on the publisher device 600 with the information “AdSet 1 has been modified based on rule 12.” The digital campaignmodification system 104 determines what to include in the informationdisplayed according to the notification options selected by thepublisher.

While the displayed notification 604 is shown as a notification alert onthe publisher device 600, the digital campaign modification system 104can provide a notification for display on the publisher device 600according to a variety of delivery methods. For example, the digitalcampaign modification system 104 can provide a notification via anemail, SMS text, or notification alert. The publisher can select thedelivery method when selecting the notification options.

As mentioned above, in one or more embodiments the digital campaignmodification system 104 utilizes a machine learning model to generateone or more suggestions. Indeed, the digital campaign modificationsystem 104 can train the machine learning model to providesuggestions/recommendations to a publisher device. For example, FIG. 7illustrates training a machine learning model in accordance with one ormore embodiments. In particular, FIG. 7 shows a plurality of contentdistribution campaigns 702 a-702 n (e.g., historical contentdistribution campaigns), a machine learning model 708, ground truthrules 712, predicted rules 710, a loss function 714, and a trainedmachine learning model 716.

As shown, the digital campaign modification system 104 receives aplurality of content distribution campaigns 702 a-702 n. For example,the digital campaign modification system 104 can access a repository ofhistorical data for content distribution campaigns implemented by anetworking system. While FIG. 7 shows three content distributioncampaigns, the plurality may include hundreds or thousands of digitalcontent distribution campaigns.

As shown in FIG. 7 , the plurality of content distribution campaigns 702a-702 n includes campaign data 706 a-706 n. Campaign data can include avariety of characteristics or features of a content distributioncampaign. For example, as shown in FIG. 7 , the campaign data 706 a-706n includes digital content, a target audience, a budget, a distributionavenue, and a campaign duration, corresponding to each contentdistribution campaign. The campaign data 706 a-706 n can includeadditional, fewer, or different information. For example, campaign datacan include any variety of campaign parameters (e.g., bidding scheduleor campaign goals) or data regarding the publisher implementing thedigital content campaign (e.g., business size, industry, age, productsor services offered, or revenue).

The training for the machine learning model 700 then passes theplurality of campaign data 706 a-706 n for each of the plurality ofcontent distribution campaigns 702 a-702 n to the machine learning model708 (e.g., applies the machine learning model 708 to the campaign data706 a-706 n). The machine learning model 708 analyzes the campaign data706 a-706 n and generates one or more predicted rules (e.g., thepredicted rules 710).

The digital campaign modification system 104 trains the machine learningmodel 708 (i.e., generates the trained machine learning model 716) bycomparing the predicted rules 710 with the ground truth rules 712. Forexample, digital campaign modification system 104 can access the groundtruth rules 712 from a repository of historical data regarding thecontent distribution campaigns 702 a-702 n. Specifically, the digitalcampaign modification system 104 can identify ground truth rulesselected and applied by publishers for the content distributioncampaigns 702 a-702 n. The digital campaign modification system 104compares the ground truth rules 712 with the predicted rules 710.

When comparing the predicted rules 710 to the ground truth rules 712,the training for the machine learning model 700 identifies a differencebetween the predicted rules 710 and the ground truth rules 712. Forexample, the digital campaign modification system 104 can apply a lossfunction 714 to determine a measure of error between the predicted rules710 and the ground truth rules 712.

The digital campaign modification system 104 can then utilize themeasure of error from applying the loss function 714 to generate thetrained machine learning model 716. Specifically, the digital campaignmodification system 104 can modify parameters of the machine learningmodel 708 to minimize the loss function 714. Thus, the machine learningmodel 708 can learn parameters that will minimize the difference betweenpredicted rules and ground truth rules. In this manner, the digitalcampaign modification system 104 can train a machine learning model topredict suggested rules that correspond to ground truth rules.

Although FIG. 7 illustrates training a machine learning model to predict“rules,” the digital campaign modification system 104 can train amachine learning model to predict custom rules and/or individualcomponents of custom rules. For example, the digital campaignmodification system 104 can generate a trained machine learning modelthat predicts triggering conditions, actions, frequency, and/ornotifications.

In one or more embodiments, the digital campaign modification system 104trains the machine learning model 708 to predict rules that improve theperformance of a digital content campaign. For example, the digitalcampaign modification system 104 can identify digital content campaignswith elevated/improved performance metrics and utilize those digitalcontent campaigns (e.g., campaign data from the digital contentcampaigns and corresponding ground truth rules) to generate the trainedmachine learning model 716. The machine learning model 716 can thuslearn to predict rules that improve digital content campaigns.

The digital campaign modification system 104 can utilize the trainedmachine learning model 716 to provide suggestions/recommendations to thepublisher. Indeed, in one or more embodiments, the digital campaignmodification system 104 uses the trained machine learning model 716 todeliver a suggestion to a publisher with regard to a new digital contentcampaign. For example, FIG. 8 illustrates utilizing a trained machinelearning model to generate a suggestion for a digital content campaign.

Indeed, as shown in FIG. 8 , the digital campaign modification system104 applies the trained machine learning model 716 to a new campaign802. Specifically, the digital campaign modification system 104 appliesthe trained machine learning model 716 to campaign data 802 acorresponding to the new campaign 802. The campaign data 802 a isspecific to the new campaign 802 but can include any of the campaigndata measures discussed above in relation to FIG. 7 (e.g., digitalcontent, target audience, business information).

The trained machine learning model 716 analyzes the campaign data 802 aand generates the suggested rules 808. Indeed, the trained machinelearning model 716 applies the parameters learned during the trainingprocess (as discussed in relation FIG. 7 ) to generate the suggestedrules 808 specific to the campaign data 802 a. As shown, the suggestedrules 808 can include trigger conditions and/or actions for the newcampaign 802 (or other components of a custom rule, such asnotifications, frequency, etc.). Moreover, as discussed above, thedigital campaign modification system 104 can provide thesuggestions/recommendations to the publisher (via a publisher device).

In one or more embodiments, the digital campaign modification system 104can continuously train the machine learning model 708. For example, thedigital campaign modification system 104 can detect custom rules (e.g.,triggering conditions and/or actions) selected by a publisher. Thedigital campaign modification system 104 can then utilize the detectedcustom rules together with digital content campaigns associated with thepublisher to further train the machine learning model 708.

Furthermore, in one or more embodiments, the digital campaignmodification system 104 can update suggestions/recommendations (e.g., inreal time or periodically). For instance, in response to receivingadditional data associated with the campaign data 802 a for the newcampaign 802 the digital campaign modification system 104 can use thetrained machine learning model 716 to update the suggested rules 808.

Turning now to FIG. 9 , additional detail will be provided regardingcomponents and functionality of the digital campaign modification system104 in accordance with one or more embodiments. In particular, FIG. 9illustrates a computing device 900 implementing the digital campaignmodification system 104. The computing device 900 can include, forexample, the server 102, the user devices 116 a-116 n, the publisherdevice 114 a-114 n, the computing device 300, and/or the publisherdevice 600.

As shown in FIG. 9 , the digital campaign modification system 104 mayinclude a user interface manager 904, a user input detector 906, anauction manager 908, a custom rule generator 910, a content distributioncampaign generator 912, an activity monitor 914, a content distributioncampaign modifier 916, a machine learning manager 918, a storage manager920 (including a machine learning model 922 and custom rules 924), and asocial graph 926 (including node information 928 and edge information930).

As just mentioned, the digital campaign modification system 104 caninclude a user interface manager 904. The user interface manager 904 canprovide, manage, and/or control a graphical user interface (or simply“user interface”) for use with the digital campaign modification system104 (e.g., the user interfaces 304, 314, 320, and 500). In particular,the user interface manager 702 may facilitate presentation ofinformation by way of an external component of a client device. Forexample, the user interface manager 904 may display a user interface byway of a display screen associated with the client device.

The user interface may be composed of a plurality of graphicalcomponents, objects, and/or elements that allow a user to perform afunction (e.g., a color modifier element, a message drafting element, ora message thread area as described above). The user interface manager904 can present, via the client device, a variety of types ofinformation, including text, images, video, audio, characters,standardized image characters, customized image characters, or otherinformation. Moreover, the user interface manager 904 can provide avariety of user interfaces specific to any variety of functions,programs, applications, plug-ins, devices, operating systems, and/orcomponents of the client device. Additional details with respect tovarious example user interface elements are described throughout withregard to various embodiments containing user interfaces.

As illustrated in FIG. 9 , the digital campaign modification system 104also includes the user input detector 906. The user input detector 906can detect, identify, monitor, receive, process, capture, and/or recordvarious types of user input. For example, the user input detector 906may be configured to detect one or more user interactions with respectto a user interface. As referred to herein, a “user interaction” refersto conduct performed by a user (or a lack of conduct performed by auser) to control the function of a computing device. “User input,” asused herein, refers to input data generated in response to a userinteraction.

The user input detector 906 can operate in conjunction with any numberof user input devices or computing devices (in isolation or incombination), including personal computers, laptops, smartphones, smartwatches, tablets, touchscreen devices, televisions, personal digitalassistants, mouse devices, keyboards, track pads, or stylus devices. Theuser input detector 906 can detect and identify various types of userinteractions with user input devices, such as select events, dragevents, scroll events, and so forth. The user input detector 906 canalso detect a user interaction with respect to a variety of userinterface elements. For example, the customized image character system100 can detect user interaction with a triggering event and/or action togenerate a custom rule.

As shown in FIG. 9 , the digital campaign modification system 104 alsoincludes the auction manager 908. Specifically, the auction manager 908can operate, execute, and/or run an online automated auction. Asdescribed above, the auction manager 908 can generate bids based oncampaign parameters of various publishers and identify a winning bidder.

As mentioned above, the digital campaign modification system 104includes the custom rule generator 910. The custom rule generator 910can create, determine, and/or generate custom rules, includingtriggering conditions and/or events. For example, the custom rulegenerator 910 can determine triggering conditions and/or actionsselected by a publisher and generate a custom rule. Moreover, the customrule generator 910 may associate the custom rule with one or moredigital content distribution campaigns of the publisher.

As illustrated in FIG. 9 , the digital campaign modification system 104may also include the content distribution campaign generator 912. Thecontent distribution campaign generator 912 may create and/or generate acontent distribution campaign. For example, the content distributioncampaign generator 912 can identify campaign parameters and digitalcontent from a publisher and generate a content distribution campaign.

In addition, as shown in FIG. 9 , the digital campaign modificationsystem 104 can also include the activity monitor 914. The activitymonitor 914 can monitor, track, and/or identify activity related to acontent distribution campaign. For instance, the activity monitor 914may monitor activity on a plurality of user devices, including activitywith digital content provided by a publisher. Moreover, the activitymonitor 914 can monitor budget expenditures, impressions purchases,digital content delivered, or other activities corresponding to acontent distribution campaign. The activity monitor 914 can monitoractivities to detect satisfaction of a triggering condition.

Moreover, as illustrated in FIG. 9 , the digital campaign modificationsystem 104 also includes the content distribution campaign modifier 916.The content distribution campaign modifier 916 can change, update,modify, and/or alter a content distribution campaign. In particular, thecontent distribution campaign modifier 916 can modify a contentdistribution campaign according the action(s) in a custom rule.Specifically, in response to detecting satisfaction of a triggeringcondition (e.g., via the activity monitor 914) the content distributioncampaign modifier 916 can apply an action corresponding to thetriggering condition to modify a content distribution campaign.

As illustrated in FIG. 9 , the digital campaign modification system 104can include the machine learning manager 922. The machine learningmanager 922 can generate, train, apply, and/or teach a machine learningmodel (e.g., the machine learning model 708 and/or the trained machinelearning model 716). For instance, the machine learning manager 922 cantrain a machine learning model to predict rules (e.g., triggeringconditions and/or actions). Moreover, the machine learning manager 922can apply the trained machine learning model to all or part of a digitalcontent campaign to generated suggested rules.

As shown in FIG. 9 , the digital campaign modification system 104 alsoincludes the storage manager 920. The storage manager 920 can maintaindata of any type, size, or kind, as necessary to perform the functionsof the digital campaign modification system 104. As illustrated, thestorage manager 920 can store the trained machine learning model 922 orthe custom rules 924 (e.g., custom rules of one or more contentdistribution campaigns). Although not illustrated, the storage manager920 can maintain other data, including campaign parameters, historicaldatabases, or other information discussed herein.

As further shown in FIG. 9 , the digital campaign modification system104 includes the social graph 926, which includes node information 928and edge information 930. The social graph 926 can include nodeinformation 928 that stores information comprising nodes for users,nodes for concepts, and/or nodes for content items. In addition, thesocial graph 926 can include edge information 930 comprisingrelationships between nodes and/or actions occurring within thesocial-networking system. Further detail regarding social-networkingsystems, social graphs, edges, and nodes is presented below in relationto FIG. 13 .

The digital campaign modification system 104 can communicate with thesocial graph 926 to perform its functions. For example, the digitalcampaign modification system 104 can utilize the social graph 926 toobtain impression opportunities and provide content items. For instance,the digital campaign modification system 104 can obtain user informationthat includes specific details regarding the potential users to whom acontent item is to be potentially distributed. Accordingly, the digitalcampaign modification system 104 can use the received information tomatch targeting parameters (e.g., the target audience) in distributingthe digital content of the digital content distribution campaign.

Each of the components 904-930 of the digital campaign modificationsystem 104 and their corresponding elements may be in communication withone another using any suitable communication technologies. It will berecognized that although components 902-930 and their correspondingelements are shown to be separate in FIG. 9 , any of components 902-930and their corresponding elements may be combined into fewer components,such as into a single facility or module, divided into more components,or configured into different components as may serve a particularembodiment.

The components 904-930 and their corresponding elements can comprisesoftware, hardware, or both. For example, the components 904-930 andtheir corresponding elements can comprise one or more instructionsstored on a computer-readable storage medium and executable byprocessors of one or more computing devices. The components 902-930 andtheir corresponding elements can comprise hardware, such as a specialpurpose processing device to perform a certain function or group offunctions. Additionally, or alternatively, the components 902-930 andtheir corresponding elements can comprise a combination ofcomputer-executable instructions and hardware.

Furthermore, the components 904-930 of the digital campaign modificationsystem 104 may, for example, be implemented as one or more stand-aloneapplications, as one or more modules of an application, as one or moreplug-ins, as one or more library functions or functions that may becalled by other applications, and/or as a cloud-computing model. Thus,the components 904-930 of the digital campaign modification system 104may be implemented as a stand-alone application, such as a desktop ormobile application. Furthermore, the components 904-930 of the digitalcampaign modification system 104 may be implemented as one or moreweb-based applications hosted on a remote server. Alternatively, oradditionally, the components of the digital campaign modification system104 may be implemented in a suit of mobile device applications or“apps.”

FIGS. 1-9 , the corresponding text, and the examples provide a number ofdifferent methods, systems, devices, and non-transitory computerreadable media for implementing the digital campaign modification system104. FIG. 10 illustrates a flowchart of a series of acts 1000 ofimplementing the digital campaign modification system 104 for a digitalcontent distribution campaign in accordance with one or moreembodiments. While FIG. 10 illustrates acts according to one embodiment,alternative embodiments may omit, add to, reorder, and/or modify any ofthe acts shown in FIG. 10 . The acts of FIG. 10 can be performed as partof a method. Alternatively, a non-transitory computer readable mediumcan comprise instructions, that when executed by one or more processors,cause a computing device to perform the acts of FIG. 10 . In someembodiments, a system can perform the acts of FIG. 10 .

The series of acts 1000 includes an act 1010 of providing a userinterface with triggering condition(s) and action(s) corresponding to acontent distribution campaign. In particular, the act 1010 includesproviding for display, to a publisher device, a user interfacecomprising a plurality of triggering conditions corresponding to acontent distribution campaign and a plurality of actions correspondingto the content distribution campaign. For example, in one or moreembodiments, the plurality of triggering conditions comprises at leastone of a budget threshold, a cost threshold, or an impressionsthreshold. Additionally, in one or more embodiments, the plurality ofactions comprises at least one of a modify budget action, a modifytarget audience action, a modify bid amount action, or a pause contentdistribution campaign action. Moreover, the act 1010 includes whereinproviding for display, to the publisher device, the user interfacecomprises providing a notification option via the user interface.

As illustrated, the series of acts 1000 also includes the act 1020 ofgenerating a custom rule in response to the selected triggers andactions. In particular, the act 1020 includes in response to userselection of a triggering condition from the plurality of triggeringconditions and user selection of an action from the plurality ofactions, generating a custom rule operable to modify the contentdistribution campaign, the custom rule comprising the triggeringcondition and the action. For example, in one or more embodiments, theact 1020 includes detecting user selection of a notification option withthe triggering condition.

In addition, as illustrated in FIG. 10 , the series of acts 1000includes the act 1030 of monitoring activity corresponding to thecontent distribution campaign. Specifically, the act 1030 can include,upon executing the content distribution campaign, monitoring activitycorresponding to the content distribution campaign to detectsatisfaction of the triggering condition. For example, the act 1030 caninclude monitoring the activity in accordance with a rate of monitoringprovided by a user.

As shown in FIG. 10 , the series of acts 1000 also includes the act 1040of detecting satisfaction of the triggering condition(s). In particular,the act 1040 includes detecting, based on the monitored activity, thatone or more of the triggering conditions are satisfied. For example, theact 1040 can include monitoring user activity corresponding to aplurality of client devices to detect satisfaction of a triggeringcondition.

As illustrated in FIG. 10 , the series of acts 1000 also includes theact 1050 of modifying the content distribution campaign. Specifically,the act 1050 includes modifying the content distribution campaignaccording to the actions of the custom rule. For example, the act 1050includes in response to detecting satisfaction of the triggeringcondition, automatically modifying the content distribution campaignaccording to the action of the custom rule. Moreover, the act 1050 canalso include in response to detecting satisfaction of the triggeringcondition automatically providing a notification according to thenotification option.

Moreover, as shown in FIG. 10 , the series of acts 1000 includes the act1060 of executing the modified content distribution campaign. Inparticular, the act 1060 includes in response to detecting satisfactionof the triggering condition, automatically executing the modifiedcontent distribution campaign. For example, the act 1060 includesdistributing digital content according to the modified contentdistribution campaign.

In one or more embodiments, providing for display, to the publisherdevice, the user interface comprises providing a plurality of contentdistribution campaigns for display via the user interface. Moreover, theseries of acts 1000 includes, in response to user selection of at leasttwo content distribution campaigns, generating a custom rule formodifying each of the at least two content distribution campaigns.Furthermore, the series of acts 1000 includes upon executing each of theat least two content distribution campaigns, monitoring activitycorresponding to each of the at least two content distribution campaignsfrom the plurality to detect satisfaction of the triggering condition.

Additionally, in one or more embodiments, the series of acts 1000includes using a trained machine learning model to provide suggestionsto the user. In particular, the series of acts 1000 includes generatinga suggested triggering condition and a corresponding suggested action byanalyzing the content distribution campaign utilizing a machine learningmodel trained based on a plurality of historical content distributioncampaigns. For example, the series of acts 1000 includes providing thesuggested triggering condition and the corresponding suggested actionfor display to the publisher device via the user interface.

Embodiments of the present disclosure may comprise or utilize a specialpurpose or general-purpose computer including computer hardware, suchas, for example, one or more processors and system memory, as discussedin greater detail below. Embodiments within the scope of the presentdisclosure also include physical and other computer-readable media forcarrying or storing computer-executable instructions and/or datastructures. In particular, one or more of the processes described hereinmay be implemented at least in part as instructions embodied in anon-transitory computer-readable medium and executable by one or morecomputing devices (e.g., any of the media content access devicesdescribed herein). In general, a processor (e.g., a microprocessor)receives instructions, from a non-transitory computer-readable medium,(e.g., a memory, etc.), and executes those instructions, therebyperforming one or more processes, including one or more of the processesdescribed herein.

Computer-readable media can be any available media that can be accessedby a general purpose or special purpose computer system.Computer-readable media that store computer-executable instructions arenon-transitory computer-readable storage media (devices).Computer-readable media that carry computer-executable instructions aretransmission media. Thus, by way of example, and not limitation,embodiments of the disclosure can comprise at least two distinctlydifferent kinds of computer-readable media: non-transitorycomputer-readable storage media (devices) and transmission media.

Non-transitory computer-readable storage media (devices) includes RAM,ROM, EEPROM, CD-ROM, solid state drives (“SSDs”) (e.g., based on RAM),Flash memory, phase-change memory (“PCM”), other types of memory, otheroptical disk storage, magnetic disk storage or other magnetic storagedevices, or any other medium which can be used to store desired programcode means in the form of computer-executable instructions or datastructures and which can be accessed by a general purpose or specialpurpose computer.

A “network” is defined as one or more data links that enable thetransport of electronic data between computer systems and/or modulesand/or other electronic devices. When information is transferred orprovided over a network or another communications connection (eitherhardwired, wireless, or a combination of hardwired or wireless) to acomputer, the computer properly views the connection as a transmissionmedium. Transmissions media can include a network and/or data linkswhich can be used to carry desired program code means in the form ofcomputer-executable instructions or data structures and which can beaccessed by a general purpose or special purpose computer. Combinationsof the above should also be included within the scope ofcomputer-readable media.

Further, upon reaching various computer system components, program codemeans in the form of computer-executable instructions or data structurescan be transferred automatically from transmission media tonon-transitory computer-readable storage media (devices) (or viceversa). For example, computer-executable instructions or data structuresreceived over a network or data link can be buffered in RAM within anetwork interface module (e.g., a “NIC”), and then eventuallytransferred to computer system RAM and/or to less volatile computerstorage media (devices) at a computer system. Thus, it should beunderstood that non-transitory computer-readable storage media (devices)can be included in computer system components that also (or evenprimarily) utilize transmission media.

Computer-executable instructions comprise, for example, instructions anddata which, when executed at a processor, cause a general-purposecomputer, special purpose computer, or special purpose processing deviceto perform a certain function or group of functions. In someembodiments, computer-executable instructions are executed on ageneral-purpose computer to turn the general-purpose computer into aspecial purpose computer implementing elements of the disclosure. Thecomputer executable instructions may be, for example, binaries,intermediate format instructions such as assembly language, or evensource code. Although the subject matter has been described in languagespecific to structural features and/or methodological acts, it is to beunderstood that the subject matter defined in the appended claims is notnecessarily limited to the described features or acts described above.Rather, the described features and acts are disclosed as example formsof implementing the claims.

Those skilled in the art will appreciate that the disclosure may bepracticed in network computing environments with many types of computersystem configurations, including, personal computers, desktop computers,laptop computers, message processors, hand-held devices, multi-processorsystems, microprocessor-based or programmable consumer electronics,network PCs, minicomputers, mainframe computers, mobile telephones,PDAs, tablets, pagers, routers, switches, and the like. The disclosuremay also be practiced in distributed system environments where local andremote computer systems, which are linked (either by hardwired datalinks, wireless data links, or by a combination of hardwired andwireless data links) through a network, both perform tasks. In adistributed system environment, program modules may be located in bothlocal and remote memory storage devices.

Embodiments of the present disclosure can also be implemented in cloudcomputing environments. In this description, “cloud computing” isdefined as a model for enabling on-demand network access to a sharedpool of configurable computing resources. For example, cloud computingcan be employed in the marketplace to offer ubiquitous and convenienton-demand access to the shared pool of configurable computing resources.The shared pool of configurable computing resources can be rapidlyprovisioned via virtualization and released with low management effortor service provider interaction, and then scaled accordingly.

A cloud-computing model can be composed of various characteristics suchas, for example, on-demand self-service, broad network access, resourcepooling, rapid elasticity, measured service, and so forth. Acloud-computing model can also expose various service models, such as,for example, Software as a Service (“SaaS”), Platform as a Service(“PaaS”), and Infrastructure as a Service (“IaaS”). A cloud-computingmodel can also be deployed using different deployment models such asprivate cloud, community cloud, public cloud, hybrid cloud, and soforth. In this description and in the claims, a “cloud-computingenvironment” is an environment in which cloud computing is employed.

FIG. 11 illustrates a block diagram of exemplary computing device 1100that may be configured to perform one or more of the processes describedabove. One will appreciate that one or more computing devices such asthe computing device 1100 may implement one or more components thedigital video insertion system 112. As shown by FIG. 11 , the computingdevice 1100 can comprise a processor 1102, a memory 1104, a storagedevice 1106, an I/O interface 1108, and a communication interface 1110,which may be communicatively coupled by way of a communicationinfrastructure 1112. While an exemplary computing device 1100 is shownin FIG. 11 , the components illustrated in FIG. 11 are not intended tobe limiting. Additional or alternative components may be used in otherembodiments. Furthermore, in certain embodiments, the computing device1100 can include fewer components than those shown in FIG. 11 .Components of the computing device 1100 shown in FIG. 11 will now bedescribed in additional detail.

In one or more embodiments, the processor 1102 includes hardware forexecuting instructions, such as those making up a computer program. Asan example and not by way of limitation, to execute instructions, theprocessor 1102 may retrieve (or fetch) the instructions from an internalregister, an internal cache, the memory 1104, or the storage device 1106and decode and execute them. In one or more embodiments, the processor1102 may include one or more internal caches for data, instructions, oraddresses. As an example and not by way of limitation, the processor1102 may include one or more instruction caches, one or more datacaches, and one or more translation lookaside buffers (TLBs).Instructions in the instruction caches may be copies of instructions inthe memory 1104 or the storage device 1106.

The memory 1104 may be used for storing data, metadata, and programs forexecution by the processor(s). The memory 1104 may include one or moreof volatile and non-volatile memories, such as Random Access Memory(“RAM”), Read Only Memory (“ROM”), a solid-state disk (“SSD”), Flash,Phase Change Memory (“PCM”), or other types of data storage. The memory1104 may be internal or distributed memory.

The storage device 1106 includes storage for storing data orinstructions. As an example and not by way of limitation, storage device1106 can comprise a non-transitory storage medium described above. Thestorage device 1106 may include a hard disk drive (HDD), a floppy diskdrive, flash memory, an optical disc, a magneto-optical disc, magnetictape, or a Universal Serial Bus (USB) drive or a combination of two ormore of these. The storage device 1106 may include removable ornon-removable (or fixed) media, where appropriate. The storage device1106 may be internal or external to the computing device 1100. In one ormore embodiments, the storage device 1106 is non-volatile, solid-statememory. In other embodiments, the storage device 1106 includes read-onlymemory (ROM). Where appropriate, this ROM may be mask programmed ROM,programmable ROM (PROM), erasable PROM (EPROM), electrically erasablePROM (EEPROM), electrically alterable ROM (EAROM), or flash memory or acombination of two or more of these.

The I/O interface 1108 allows a user to provide input to, receive outputfrom, and otherwise transfer data to and receive data from computingdevice 1100. The I/O interface 1108 may include a mouse, a keypad or akeyboard, a touch screen, a camera, an optical scanner, networkinterface, modem, other known I/O devices or a combination of such I/Ointerfaces. The I/O interface 1108 may include one or more devices forpresenting output to a user, including, but not limited to, a graphicsengine, a display (e.g., a display screen), one or more output drivers(e.g., display drivers), one or more audio speakers, and one or moreaudio drivers. In certain embodiments, the I/O interface 1108 isconfigured to provide graphical data to a display for presentation to auser. The graphical data may be representative of one or more graphicaluser interfaces and/or any other graphical content as may serve aparticular implementation.

The communication interface 1110 can include hardware, software, orboth. In any event, the communication interface 1110 can provide one ormore interfaces for communication (such as, for example, packet-basedcommunication) between the computing device 1100 and one or more othercomputing devices or networks. As an example and not by way oflimitation, the communication interface 1110 may include a networkinterface controller (NIC) or network adapter for communicating with anEthernet or other wire-based network or a wireless NIC (WNIC) orwireless adapter for communicating with a wireless network, such as aWI-FI.

Additionally or alternatively, the communication interface 1110 mayfacilitate communications with an ad hoc network, a personal areanetwork (PAN), a local area network (LAN), a wide area network (WAN), ametropolitan area network (MAN), or one or more portions of the Internetor a combination of two or more of these. One or more portions of one ormore of these networks may be wired or wireless. As an example, thecommunication interface 1110 may facilitate communications with awireless PAN (WPAN) (such as, for example, a BLUETOOTH WPAN), a WI-FInetwork, a WI-MAX network, a cellular telephone network (such as, forexample, a Global System for Mobile Communications (GSM) network), orother suitable wireless network or a combination thereof.

Additionally, the communication interface 1110 may facilitatecommunications various communication protocols. Examples ofcommunication protocols that may be used include, but are not limitedto, data transmission media, communications devices, TransmissionControl Protocol (“TCP”), Internet Protocol (“IP”), File TransferProtocol (“FTP”), Telnet, Hypertext Transfer Protocol (“HTTP”),Hypertext Transfer Protocol Secure (“HTTPS”), Session InitiationProtocol (“SIP”), Simple Object Access Protocol (“SOAP”), ExtensibleMark-up Language (“XML”) and variations thereof, Simple Mail TransferProtocol (“SMTP”), Real-Time Transport Protocol (“RTP”), User DatagramProtocol (“UDP”), Global System for Mobile Communications (“GSM”)technologies, Code Division Multiple Access (“CDMA”) technologies, TimeDivision Multiple Access (“TDMA”) technologies, Short Message Service(“SMS”), Multimedia Message Service (“MMS”), radio frequency (“RF”)signaling technologies, Long Term Evolution (“LTE”) technologies,wireless communication technologies, in-band and out-of-band signalingtechnologies, and other suitable communications networks andtechnologies.

The communication infrastructure 1112 may include hardware, software, orboth that couples components of the computing device 1100 to each other.As an example and not by way of limitation, the communicationinfrastructure 1112 may include an Accelerated Graphics Port (AGP) orother graphics bus, an Enhanced Industry Standard Architecture (EISA)bus, a front-side bus (FSB), a HYPERTRANSPORT (HT) interconnect, anIndustry Standard Architecture (ISA) bus, an INFINIBAND interconnect, alow-pin-count (LPC) bus, a memory bus, a Micro Channel Architecture(MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express(PCIe) bus, a serial advanced technology attachment (SATA) bus, a VideoElectronics Standards Association local (VLB) bus, or another suitablebus or a combination thereof.

As mentioned above, in one or more embodiments, the digital campaignmodification system 104 operates in connection with a social networkingsystem (e.g., the networking system 110 as described with reference toFIG. 1 ). In addition to the description given above, a socialnetworking system may enable its users (such as persons ororganizations) to interact with the system and with each other. Thesocial networking system may, with input from a user, create and storein the social networking system a user profile associated with the user.The user profile may include demographic information,communication-channel information, and information on personal interestsof the user. The social networking system may also, with input from auser, create and store a record of relationships of the user with otherusers of the social networking system, as well as provide services(e.g., posts, photo-sharing, event organization, messaging, games, oradvertisements) to facilitate social interaction between or among users.

The social networking system may store records of users andrelationships between users in a social graph comprising a plurality ofnodes and a plurality of edges connecting the nodes. The nodes maycomprise a plurality of user nodes and a plurality of concept nodes. Auser node of the social graph may correspond to a user of the socialnetworking system. A user may be an individual (human user), an entity(e.g., an enterprise, business, or third party application), or a group(e.g., of individuals or entities). A user node corresponding to a usermay comprise information provided by the user and information gatheredby various systems, including the social networking system.

For example, the user may provide his or her name, profile picture, cityof residence, contact information, birth date, gender, marital status,family status, employment, educational background, preferences,interests, and other demographic information to be included in the usernode. Each user node of the social graph may have a corresponding webpage (typically known as a profile page). In response to a requestincluding a user name, the social networking system can access a usernode corresponding to the user name, and construct a profile pageincluding the name, a profile picture, and other information associatedwith the user. A profile page of a first user may display to a seconduser all or a portion of the first user's information based on one ormore privacy settings by the first user and the relationship between thefirst user and the second user.

A concept node may correspond to a concept of the social networkingsystem. For example, a concept can represent a real-world entity, suchas a movie, a song, a sports team, a celebrity, a group, a restaurant,or a place or a location. An administrative user of a concept nodecorresponding to a concept may create or update the concept node byproviding information of the concept (e.g., by filling out an onlineform), causing the social networking system to associate the informationwith the concept node. For example and without limitation, informationassociated with a concept can include a name or a title, one or moreimages (e.g., an image of cover page of a book), a web site (e.g., anURL address) or contact information (e.g., a phone number, an emailaddress). Each concept node of the social graph may correspond to a webpage. For example, in response to a request including a name, the socialnetworking system can access a concept node corresponding to the name,and construct a web page including the name and other informationassociated with the concept.

An edge between a pair of nodes may represent a relationship between thepair of nodes. For example, an edge between two user nodes can representa friendship between two users. For another example, the socialnetworking system may construct a web page (or a structured document) ofa concept node (e.g., a restaurant, a celebrity), incorporating one ormore selectable option or selectable elements (e.g., “like”, “check in”)in the web page. A user can access the page using a web browser hostedby the user's client device and select a selectable option or selectableelement, causing the client device to transmit to the social networkingsystem a request to create an edge between a user node of the user and aconcept node of the concept, indicating a relationship between the userand the concept (e.g., the user checks in a restaurant, or the user“likes” a celebrity).

As an example, a user may provide (or change) his or her city ofresidence, causing the social networking system to create an edgebetween a user node corresponding to the user and a concept nodecorresponding to the city declared by the user as his or her city ofresidence. In addition, the degree of separation between any two nodesis defined as the minimum number of hops required to traverse the socialgraph from one node to the other. A degree of separation between twonodes can be considered a measure of relatedness between the users orthe concepts represented by the two nodes in the social graph. Forexample, two users having user nodes that are directly connected by anedge (i.e., are first-degree nodes) may be described as “connectedusers” or “friends.” Similarly, two users having user nodes that areconnected only through another user node (i.e., are second-degree nodes)may be described as “friends of friends.”

A social networking system may support a variety of applications, suchas photo sharing, on-line calendars and events, gaming, instantmessaging, and advertising. For example, the social networking systemmay also include media sharing capabilities. Also, the social networkingsystem may allow users to post photographs and other multimedia contentitems to a user's profile page (typically known as “wall posts” or“timeline posts”) or in a photo album, both of which may be accessibleto other users of the social networking system depending upon the user'sconfigured privacy settings. The social networking system may also allowusers to configure events. For example, a first user may configure anevent with attributes including time and date of the event, location ofthe event and other users invited to the event. The invited users mayreceive invitations to the event and respond (such as by accepting theinvitation or declining it). Furthermore, the social networking systemmay allow users to maintain a personal calendar. Similarly to events,the calendar entries may include times, dates, locations and identitiesof other users.

FIG. 12 illustrates an example network environment 1200 of a socialnetworking system. Network environment 1200 includes a client device1206, a networking system 1202 (e.g., a social networking system and/oran electronic messaging system), and a third-party system 1208 connectedto each other by a network 1204. Although FIG. 12 illustrates aparticular arrangement of client device 1206, networking system 1202,third-party system 1208, and network 1204, this disclosure contemplatesany suitable arrangement of client device 1206, networking system 1202,third-party system 1208, and network 1204. As an example and not by wayof limitation, two or more of client device 1206, networking system1202, and third-party system 1208 may be connected to each otherdirectly, bypassing network 1204. As another example, two or more ofclient device 1206, networking system 1202, and third-party system 1208may be physically or logically co-located with each other in whole or inpart. Moreover, although FIG. 12 illustrates a particular number ofclient devices 1206, networking systems 1202, third-party systems 1208,and networks 1204, this disclosure contemplates any suitable number ofclient devices 1206, networking systems 1202, third-party systems 1208,and networks 1204. As an example and not by way of limitation, networkenvironment 1200 may include multiple client device 1206, networkingsystems 1202, third-party systems 1208, and networks 1204.

This disclosure contemplates any suitable network 1204. As an exampleand not by way of limitation, one or more portions of network 1204 mayinclude an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local area network (LAN), a wireless LAN (WLAN), a widearea network (WAN), a wireless WAN (WWAN), a metropolitan area network(MAN), a portion of the Internet, a portion of the Public SwitchedTelephone Network (PSTN), a cellular telephone network, or a combinationof two or more of these. Network 1204 may include one or more networks1204.

Links may connect client device 1206, networking system 1202, andthird-party system 1208 to communication network 1204 or to each other.This disclosure contemplates any suitable links. In particularembodiments, one or more links include one or more wireline (such as forexample Digital Subscriber Line (DSL) or Data Over Cable ServiceInterface Specification (DOCSIS)), wireless (such as for example Wi-Fior Worldwide Interoperability for Microwave Access (WiMAX)), or optical(such as for example Synchronous Optical Network (SONET) or SynchronousDigital Hierarchy (SDH)) links. In particular embodiments, one or morelinks each include an ad hoc network, an intranet, an extranet, a VPN, aLAN, a WLAN, a WAN, a WWAN, a MAN, a portion of the Internet, a portionof the PSTN, a cellular technology-based network, a satellitecommunications technology-based network, another link, or a combinationof two or more such links. Links need not necessarily be the samethroughout network environment 1200. One or more first links may differin one or more respects from one or more second links.

In particular embodiments, client device 1206 may be an electronicdevice including hardware, software, or embedded logic components or acombination of two or more such components and capable of carrying outthe appropriate functionalities implemented or supported by clientdevice 1206. As an example and not by way of limitation, a client device1206 may include a computer system such as an augmented reality displaydevice, a desktop computer, notebook or laptop computer, netbook, atablet computer, e-book reader, GPS device, camera, personal digitalassistant (PDA), handheld electronic device, cellular telephone,smartphone, other suitable electronic device, or any suitablecombination thereof. This disclosure contemplates any suitable clientdevices 1206. A client device 1206 may enable a network user at clientdevice 1206 to access network 1204. A client device 1206 may enable itsuser to communicate with other users at other client devices 1206.

In particular embodiments, client device 1206 may include a web browser,such as MICROSOFT INTERNET EXPLORER, GOOGLE CHROME or MOZILLA FIREFOX,and may have one or more add-ons, plug-ins, or other extensions, such asTOOLBAR or YAHOO TOOLBAR. A user at client device 1206 may enter aUniform Resource Locator (URL) or other address directing the webbrowser to a particular server (such as server, or a server associatedwith a third-party system 1208), and the web browser may generate aHyper Text Transfer Protocol (HTTP) request and communicate the HTTPrequest to server. The server may accept the HTTP request andcommunicate to client device 1206 one or more Hyper Text Markup Language(HTML) files responsive to the HTTP request. Client device 1206 mayrender a webpage based on the HTML files from the server forpresentation to the user. This disclosure contemplates any suitablewebpage files. As an example and not by way of limitation, webpages mayrender from HTML files, Extensible Hyper Text Markup Language (XHTML)files, or Extensible Markup Language (XML) files, according toparticular needs. Such pages may also execute scripts such as, forexample and without limitation, those written in JAVASCRIPT, JAVA,MICROSOFT SILVERLIGHT, combinations of markup language and scripts suchas AJAX (Asynchronous JAVASCRIPT and XML), and the like. Herein,reference to a webpage encompasses one or more corresponding webpagefiles (which a browser may use to render the webpage) and vice versa,where appropriate.

In particular embodiments, networking system 1202 may be anetwork-addressable computing system that can host an online socialnetwork. Networking system 1202 may generate, store, receive, and sendsocial-networking data, such as, for example, user-profile data,concept-profile data, social-graph information, or other suitable datarelated to the online social network. Networking system 1202 may beaccessed by the other components of network environment 1200 eitherdirectly or via network 1204. In particular embodiments, networkingsystem 1202 may include one or more servers. Each server may be aunitary server or a distributed server spanning multiple computers ormultiple datacenters. Servers may be of various types, such as, forexample and without limitation, web server, news server, mail server,message server, advertising server, file server, application server,exchange server, database server, proxy server, another server suitablefor performing functions or processes described herein, or anycombination thereof. In particular embodiments, each server may includehardware, software, or embedded logic components or a combination of twoor more such components for carrying out the appropriate functionalitiesimplemented or supported by server. In particular embodiments,networking system 1202 may include one or more data stores. Data storesmay be used to store various types of information. In particularembodiments, the information stored in data stores may be organizedaccording to specific data structures. In particular embodiments, eachdata store may be a relational, columnar, correlation, or other suitabledatabase. Although this disclosure describes or illustrates particulartypes of databases, this disclosure contemplates any suitable types ofdatabases. Particular embodiments may provide interfaces that enable aclient device 1206, a networking system 1202, or a third-party system1208 to manage, retrieve, modify, add, or delete, the information storedin data store.

In particular embodiments, networking system 1202 may store one or moresocial graphs in one or more data stores. In particular embodiments, asocial graph may include multiple nodes—which may include multiple usernodes (each corresponding to a particular user) or multiple conceptnodes (each corresponding to a particular concept)—and multiple edgesconnecting the nodes. Networking system 1202 may provide users of theonline social network the ability to communicate and interact with otherusers. In particular embodiments, users may join the online socialnetwork via networking system 1202 and then add connections (e.g.,relationships) to a number of other users of networking system 1202 thatthey want to be connected to. Herein, the term “friend” may refer to anyother user of networking system 1202 with whom a user has formed aconnection, association, or relationship via networking system 1202.

In particular embodiments, networking system 1202 may provide users withthe ability to take actions on various types of items or objects,supported by networking system 1202. As an example and not by way oflimitation, the items and objects may include groups or social networksto which users of networking system 1202 may belong, events or calendarentries in which a user might be interested, computer-based applicationsthat a user may use, transactions that allow users to buy or sell itemsvia the service, interactions with advertisements that a user mayperform, or other suitable items or objects. A user may interact withanything that is capable of being represented in networking system 1202or by an external system of third-party system 1208, which is separatefrom networking system 1202 and coupled to networking system 1202 via anetwork 1204.

In particular embodiments, networking system 1202 may be capable oflinking a variety of entities. As an example and not by way oflimitation, networking system 1202 may enable users to interact witheach other as well as receive content from third-party systems 1208 orother entities, or to allow users to interact with these entitiesthrough an application programming interfaces (API) or othercommunication channels.

In particular embodiments, a third-party system 1208 may include one ormore types of servers, one or more data stores, one or more interfaces,including but not limited to APIs, one or more web services, one or morecontent sources, one or more networks, or any other suitable components,e.g., that servers may communicate with. A third-party system 1208 maybe operated by a different entity from an entity operating networkingsystem 1202. In particular embodiments, however, networking system 1202and third-party systems 1208 may operate in conjunction with each otherto provide social-networking services to users of networking system 1202or third-party systems 1208. In this sense, networking system 1202 mayprovide a platform, or backbone, which other systems, such asthird-party systems 1208, may use to provide social-networking servicesand functionality to users across the Internet.

In particular embodiments, a third-party system 1208 may include athird-party content object provider. A third-party content objectprovider may include one or more sources of content objects, which maybe communicated to a client device 1206. As an example and not by way oflimitation, content objects may include information regarding things oractivities of interest to the user, such as, for example, movie showtimes, movie reviews, restaurant reviews, restaurant menus, productinformation and reviews, or other suitable information. As anotherexample and not by way of limitation, content objects may includeincentive content objects, such as coupons, discount tickets, giftcertificates, or other suitable incentive objects.

In particular embodiments, networking system 1202 also includesuser-generated content objects, which may enhance a user's interactionswith networking system 1202. User-generated content may include anythinga user can add, upload, send, or “post” to networking system 1202. As anexample and not by way of limitation, a user communicates posts tonetworking system 1202 from a client device 1206. Posts may include datasuch as status updates or other textual data, location information,photos, videos, links, music or other similar data or media. Content mayalso be added to networking system 1202 by a third-party through a“communication channel,” such as a newsfeed or stream.

In particular embodiments, networking system 1202 may include a varietyof servers, sub-systems, programs, modules, logs, and data stores. Inparticular embodiments, networking system 1202 may include one or moreof the following: a web server, action logger, API-request server,relevance-and-ranking engine, content-object classifier, notificationcontroller, action log, third-party-content-object-exposure log,inference module, authorization/privacy server, search module,advertisement-targeting module, user-interface module, user-profilestore, connection store, third-party content store, or location store.Networking system 1202 may also include suitable components such asnetwork interfaces, security mechanisms, load balancers, failoverservers, management-and-network-operations consoles, other suitablecomponents, or any suitable combination thereof. In particularembodiments, networking system 1202 may include one or more user-profilestores for storing user profiles. A user profile may include, forexample, biographic information, demographic information, behavioralinformation, social information, or other types of descriptiveinformation, such as work experience, educational history, hobbies orpreferences, interests, affinities, or location. Interest informationmay include interests related to one or more categories. Categories maybe general or specific. As an example and not by way of limitation, if auser “likes” an article about a brand of shoes the category may be thebrand, or the general category of “shoes” or “clothing.” A connectionstore may be used for storing connection information about users. Theconnection information may indicate users who have similar or commonwork experience, group memberships, hobbies, educational history, or arein any way related or share common attributes. The connectioninformation may also include user-defined connections between differentusers and content (both internal and external). A web server may be usedfor linking networking system 1202 to one or more client devices 1206 orone or more third-party system 1208 via network 1204. The web server mayinclude a mail server or other messaging functionality for receiving androuting messages between networking system 1202 and one or more clientdevices 1206. An API-request server may allow a third-party system 1208to access information from networking system 1202 by calling one or moreAPIs. An action logger may be used to receive communications from a webserver about a user's actions on or off networking system 1202. Inconjunction with the action log, a third-party-content-object log may bemaintained of user exposures to third-party-content objects. Anotification controller may provide information regarding contentobjects to a client device 1206. Information may be pushed to a clientdevice 1206 as notifications, or information may be pulled from clientdevice 1206 responsive to a request received from client device 1206.Authorization servers may be used to enforce one or more privacysettings of the users of networking system 1202. A privacy setting of auser determines how particular information associated with a user can beshared. The authorization server may allow users to opt in to or opt outof having their actions logged by networking system 1202 or shared withother systems (e.g., third-party system 1208), such as, for example, bysetting appropriate privacy settings. Third-party-content-object storesmay be used to store content objects received from third parties, suchas a third-party system 1208. Location stores may be used for storinglocation information received from client devices 1206 associated withusers. Advertisement-pricing modules may combine social information, thecurrent time, location information, or other suitable information toprovide relevant advertisements, in the form of notifications, to auser.

FIG. 13 illustrates example social graph 1300. In particularembodiments, networking system 1202 may store one or more social graphs1300 in one or more data stores. In particular embodiments, social graph1300 may include multiple nodes—which may include multiple user nodes1302 or multiple concept nodes 1304—and multiple edges 1306 connectingthe nodes. Example social graph 1300 illustrated in FIG. 13 is shown,for didactic purposes, in a two-dimensional visual map representation.In particular embodiments, a networking system 1202, client device 1206,or third-party system 1208 may access social graph 1300 and relatedsocial-graph information for suitable applications. The nodes and edgesof social graph 1300 may be stored as data objects, for example, in adata store (such as a social-graph database). Such a data store mayinclude one or more searchable or query able indexes of nodes or edgesof social graph 1300.

In particular embodiments, a user node 1302 may correspond to a user ofnetworking system 1202. As an example and not by way of limitation, auser may be an individual (human user), an entity (e.g., an enterprise,business, or third-party application), or a group (e.g., of individualsor entities) that interacts or communicates with or over networkingsystem 1202. In particular embodiments, when a user registers for anaccount with networking system 1202, networking system 1202 may create auser node 1302 corresponding to the user, and store the user node 1302in one or more data stores. Users and user nodes 1302 described hereinmay, where appropriate, refer to registered users and user nodes 1302associated with registered users. In addition or as an alternative,users and user nodes 1302 described herein may, where appropriate, referto users that have not registered with networking system 1202. Inparticular embodiments, a user node 1302 may be associated withinformation provided by a user or information gathered by varioussystems, including networking system 1202. As an example and not by wayof limitation, a user may provide his or her name, profile picture,contact information, birth date, sex, marital status, family status,employment, education background, preferences, interests, or otherdemographic information. In particular embodiments, a user node 1302 maybe associated with one or more data objects corresponding to informationassociated with a user. In particular embodiments, a user node 1302 maycorrespond to one or more webpages.

In particular embodiments, a concept node 1304 may correspond to aconcept. As an example and not by way of limitation, a concept maycorrespond to a place (such as, for example, a movie theater,restaurant, landmark, or city); a website (such as, for example, awebsite associated with networking system 1202 or a third-party websiteassociated with a web-application server); an entity (such as, forexample, a person, business, group, sports team, or celebrity); aresource (such as, for example, an audio file, video file, digitalphoto, text file, structured document, or application) which may belocated within networking system 1202 or on an external server, such asa web-application server; real or intellectual property (such as, forexample, a sculpture, painting, movie, game, song, idea, photograph, orwritten work); a game; an activity; an idea or theory; another suitableconcept; or two or more such concepts. A concept node 1304 may beassociated with information of a concept provided by a user orinformation gathered by various systems, including networking system1202. As an example and not by way of limitation, information of aconcept may include a name or a title; one or more images (e.g., animage of the cover page of a book); a location (e.g., an address or ageographical location); a web site (which may be associated with a URL);contact information (e.g., a phone number or an email address); othersuitable concept information; or any suitable combination of suchinformation. In particular embodiments, a concept node 1304 may beassociated with one or more data objects corresponding to informationassociated with concept node 1304. In particular embodiments, a conceptnode 1304 may correspond to one or more webpages.

In particular embodiments, a node in social graph 1300 may represent orbe represented by a webpage (which may be referred to as a “profilepage”). Profile pages may be hosted by or accessible to networkingsystem 1202. Profile pages may also be hosted on third-party websitesassociated with a third-party system 1208. As an example and not by wayof limitation, a profile page corresponding to a particular externalwebpage may be the particular external webpage and the profile page maycorrespond to a particular concept node 1304. Profile pages may beviewable by all or a selected subset of other users. As an example andnot by way of limitation, a user node 1302 may have a correspondinguser-profile page in which the corresponding user may add content, makedeclarations, or otherwise express himself or herself. As anotherexample and not by way of limitation, a concept node 1304 may have acorresponding concept-profile page in which one or more users may addcontent, make declarations, or express themselves, particularly inrelation to the concept corresponding to concept node 1304.

In particular embodiments, a concept node 1304 may represent athird-party webpage or resource hosted by a third-party system 1208. Thethird-party webpage or resource may include, among other elements,content, a selectable or other icon, or other inter-actable object(which may be implemented, for example, in JavaScript, AJAX, or PHPcodes) representing an action or activity. As an example and not by wayof limitation, a third-party webpage may include a selectable icon suchas “like,” “check in,” “eat,” “recommend,” or another suitable action oractivity. A user viewing the third-party webpage may perform an actionby selecting one of the icons (e.g., “eat”), causing a client device1206 to send to networking system 1202 a message indicating the user'saction. In response to the message, networking system 1202 may create anedge (e.g., an “eat” edge) between a user node 1302 corresponding to theuser and a concept node 1304 corresponding to the third-party webpage orresource and store edge 1306 in one or more data stores.

In particular embodiments, a pair of nodes in social graph 1300 may beconnected to each other by one or more edges 1306. An edge 1306connecting a pair of nodes may represent a relationship between the pairof nodes. In particular embodiments, an edge 1306 may include orrepresent one or more data objects or attributes corresponding to therelationship between a pair of nodes. As an example and not by way oflimitation, a first user may indicate that a second user is a “friend”of the first user. In response to this indication, networking system1202 may send a “friend request” to the second user. If the second userconfirms the “friend request,” networking system 1202 may create an edge1306 connecting the first user's user node 1302 to the second user'suser node 1302 in social graph 1300 and store edge 1306 as social-graphinformation in one or more of data stores. In the example of FIG. 13 ,social graph 1300 includes an edge 1306 indicating a friend relationbetween user nodes 1302 of user “A” and user “B” and an edge indicatinga friend relation between user nodes 1302 of user “C” and user “B.”Although this disclosure describes or illustrates particular edges 1306with particular attributes connecting particular user nodes 1302, thisdisclosure contemplates any suitable edges 1306 with any suitableattributes connecting user nodes 1302. As an example and not by way oflimitation, an edge 1306 may represent a friendship, familyrelationship, business or employment relationship, fan relationship,follower relationship, visitor relationship, sub scriber relationship,superior/subordinate relationship, reciprocal relationship,non-reciprocal relationship, another suitable type of relationship, ortwo or more such relationships. Moreover, although this disclosuregenerally describes nodes as being connected, this disclosure alsodescribes users or concepts as being connected. Herein, references tousers or concepts being connected may, where appropriate, refer to thenodes corresponding to those users or concepts being connected in socialgraph 1300 by one or more edges 1306.

In particular embodiments, an edge 1306 between a user node 1302 and aconcept node 1304 may represent a particular action or activityperformed by a user associated with user node 1302 toward a conceptassociated with a concept node 1304. As an example and not by way oflimitation, as illustrated in FIG. 13 , a user may “like,” “attended,”“played,” “listened,” “cooked,” “worked at,” or “watched” a concept,each of which may correspond to an edge type or subtype. Aconcept-profile page corresponding to a concept node 1304 may include,for example, a selectable “check in” icon (such as, for example, aclickable “check in” icon) or a selectable “add to favorites” icon.Similarly, after a user clicks these icons, networking system 1202 maycreate a “favorite” edge or a “check in” edge in response to a user'saction corresponding to a respective action. As another example and notby way of limitation, a user (user “C”) may listen to a particular song(“Ramble On”) using a particular application (SPOTIFY, which is anonline music application). In this case, networking system 1202 maycreate a “listened” edge 1306 and a “used” edge (as illustrated in FIG.13 ) between user nodes 1302 corresponding to the user and concept nodes1304 corresponding to the song and application to indicate that the userlistened to the song and used the application. Moreover, networkingsystem 1202 may create a “played” edge 1306 (as illustrated in FIG. 13 )between concept nodes 1304 corresponding to the song and the applicationto indicate that the particular song was played by the particularapplication. In this case, “played” edge 1306 corresponds to an actionperformed by an external application (SPOTIFY) on an external audio file(the song “Imagine”). Although this disclosure describes particularedges 1306 with particular attributes connecting user nodes 1302 andconcept nodes 1304, this disclosure contemplates any suitable edges 1306with any suitable attributes connecting user nodes 1302 and conceptnodes 1304. Moreover, although this disclosure describes edges between auser node 1302 and a concept node 1304 representing a singlerelationship, this disclosure contemplates edges between a user node1302 and a concept node 1304 representing one or more relationships. Asan example and not by way of limitation, an edge 1306 may represent boththat a user likes and has used at a particular concept. Alternatively,another edge 1306 may represent each type of relationship (or multiplesof a single relationship) between a user node 1302 and a concept node1304 (as illustrated in FIG. 13 between user node 1302 for user “E” andconcept node 1304 for “SPOTIFY”).

In particular embodiments, networking system 1202 may create an edge1306 between a user node 1302 and a concept node 1304 in social graph1300. As an example and not by way of limitation, a user viewing aconcept-profile page (such as, for example, by using a web browser or aspecial-purpose application hosted by the user's client device 1206) mayindicate that he or she likes the concept represented by the conceptnode 1304 by clicking or selecting a “Like” icon, which may cause theuser's client device 1206 to send to networking system 1202 a messageindicating the user's liking of the concept associated with theconcept-profile page. In response to the message, networking system 1202may create an edge 1306 between user node 1302 associated with the userand concept node 1304, as illustrated by “like” edge 1306 between theuser and concept node 1304. In particular embodiments, networking system1202 may store an edge 1306 in one or more data stores. In particularembodiments, an edge 1306 may be automatically formed by networkingsystem 1202 in response to a particular user action. As an example andnot by way of limitation, if a first user uploads a picture, watches amovie, or listens to a song, an edge 1306 may be formed between usernode 1302 corresponding to the first user and concept nodes 1304corresponding to those concepts. Although this disclosure describesforming particular edges 1306 in particular manners, this disclosurecontemplates forming any suitable edges 1306 in any suitable manner.

In particular embodiments, an advertisement may be text (which may beHTML-linked), one or more images (which may be HTML-linked), one or morevideos, audio, one or more ADOBE FLASH files, a suitable combination ofthese, or any other suitable advertisement in any suitable digitalformat presented on one or more webpages, in one or more e-mails, or inconnection with search results requested by a user. In addition or as analternative, an advertisement may be one or more sponsored stories(e.g., a news-feed or ticker item on networking system 1202). Asponsored story may be a social action by a user (such as “liking” apage, “liking” or commenting on a post on a page, RSVPing to an eventassociated with a page, voting on a question posted on a page, checkingin to a place, using an application or playing a game, or “liking” orsharing a website) that an advertiser promotes, for example, by havingthe social action presented within a pre-determined area of a profilepage of a user or other page, presented with additional informationassociated with the advertiser, bumped up or otherwise highlightedwithin news feeds or tickers of other users, or otherwise promoted. Theadvertiser may pay to have the social action promoted. As an example andnot by way of limitation, advertisements may be included among thesearch results of a search-results page, where sponsored content ispromoted over non-sponsored content.

In particular embodiments, an advertisement may be requested for displaywithin social-networking-system webpages, third-party webpages, or otherpages. An advertisement may be displayed in a dedicated portion of apage, such as in a banner area at the top of the page, in a column atthe side of the page, in a GUI of the page, in a pop-up window, in adrop-down menu, in an input field of the page, over the top of contentof the page, or elsewhere with respect to the page. In addition or as analternative, an advertisement may be displayed within an application. Anadvertisement may be displayed within dedicated pages, requiring theuser to interact with or watch the advertisement before the user mayaccess a page or utilize an application. The user may, for example viewthe advertisement through a web browser.

A user may interact with an advertisement in any suitable manner. Theuser may click or otherwise select the advertisement. By selecting theadvertisement, the user may be directed to (or a browser or otherapplication being used by the user) a page associated with theadvertisement. At the page associated with the advertisement, the usermay take additional actions, such as purchasing a product or serviceassociated with the advertisement, receiving information associated withthe advertisement, or subscribing to a newsletter associated with theadvertisement. An advertisement with audio or video may be played byselecting a component of the advertisement (like a “play button”).Alternatively, by selecting the advertisement, networking system 1202may execute or modify a particular action of the user.

An advertisement may also include social-networking-system functionalitythat a user may interact with. As an example and not by way oflimitation, an advertisement may enable a user to “like” or otherwiseendorse the advertisement by selecting an icon or link associated withendorsement. As another example and not by way of limitation, anadvertisement may enable a user to search (e.g., by executing a query)for content related to the advertiser. Similarly, a user may share theadvertisement with another user (e.g., through networking system 1202)or RSVP (e.g., through networking system 1202) to an event associatedwith the advertisement. In addition or as an alternative, anadvertisement may include social-networking-system context directed tothe user. As an example and not by way of limitation, an advertisementmay display information about a friend of the user within networkingsystem 1202 who has taken an action associated with the subject matterof the advertisement.

In particular embodiments, networking system 1202 may determine thesocial-graph affinity (which may be referred to herein as “affinity”) ofvarious social-graph entities for each other. Affinity may represent thestrength of a relationship or level of interest between particularobjects associated with the online social network, such as users,concepts, content, actions, advertisements, other objects associatedwith the online social network, or any suitable combination thereof.Affinity may also be determined with respect to objects associated withthird-party systems 1208 or other suitable systems. An overall affinityfor a social-graph entity for each user, subject matter, or type ofcontent may be established. The overall affinity may change based oncontinued monitoring of the actions or relationships associated with thesocial-graph entity. Although this disclosure describes determiningparticular affinities in a particular manner, this disclosurecontemplates determining any suitable affinities in any suitable manner.

In particular embodiments, networking system 1202 may measure orquantify social-graph affinity using an affinity coefficient (which maybe referred to herein as “coefficient”). The coefficient may representor quantify the strength of a relationship between particular objectsassociated with the online social network. The coefficient may alsorepresent a probability or function that measures a predictedprobability that a user will perform a particular action based on theuser's interest in the action. In this way, a user's future actions maybe predicted based on the user's prior actions, where the coefficientmay be calculated at least in part based on the history of the user'sactions. Coefficients may be used to predict any number of actions,which may be within or outside of the online social network. As anexample and not by way of limitation, these actions may include varioustypes of communications, such as sending messages, posting content, orcommenting on content; various types of observation actions, such asaccessing or viewing profile pages, media, or other suitable content;various types of coincidence information about two or more social-graphentities, such as being in the same group, tagged in the samephotograph, checked-in at the same location, or attending the sameevent; or other suitable actions. Although this disclosure describesmeasuring affinity in a particular manner, this disclosure contemplatesmeasuring affinity in any suitable manner.

In particular embodiments, networking system 1202 may use a variety offactors to calculate a coefficient. These factors may include, forexample, user actions, types of relationships between objects, locationinformation, other suitable factors, or any combination thereof. Inparticular embodiments, different factors may be weighted differentlywhen calculating the coefficient. The weights for each factor may bestatic or the weights may change according to, for example, the user,the type of relationship, the type of action, the user's location, andso forth. Ratings for the factors may be combined according to theirweights to determine an overall coefficient for the user. As an exampleand not by way of limitation, particular user actions may be assignedboth a rating and a weight while a relationship associated with theparticular user action is assigned a rating and a correlating weight(e.g., so the weights total 100%). To calculate the coefficient of auser towards a particular object, the rating assigned to the user'sactions may comprise, for example, 60% of the overall coefficient, whilethe relationship between the user and the object may comprise 40% of theoverall coefficient. In particular embodiments, the networking system1202 may consider a variety of variables when determining weights forvarious factors used to calculate a coefficient, such as, for example,the time since information was accessed, decay factors, frequency ofaccess, relationship to information or relationship to the object aboutwhich information was accessed, relationship to social-graph entitiesconnected to the object, short- or long-term averages of user actions,user feedback, other suitable variables, or any combination thereof. Asan example and not by way of limitation, a coefficient may include adecay factor that causes the strength of the signal provided byparticular actions to decay with time, such that more recent actions aremore relevant when calculating the coefficient. The ratings and weightsmay be continuously updated based on continued tracking of the actionsupon which the coefficient is based. Any type of process or algorithmmay be employed for assigning, combining, averaging, and so forth theratings for each factor and the weights assigned to the factors. Inparticular embodiments, networking system 1202 may determinecoefficients using machine-learning algorithms trained on historicalactions and past user responses, or data farmed from users by exposingthem to various options and measuring responses. Although thisdisclosure describes calculating coefficients in a particular manner,this disclosure contemplates calculating coefficients in any suitablemanner.

In particular embodiments, networking system 1202 may calculate acoefficient based on a user's actions. Networking system 1202 maymonitor such actions on the online social network, on a third-partysystem 1208, on other suitable systems, or any combination thereof. Anysuitable type of user actions may be tracked or monitored. Typical useractions include viewing profile pages, creating or posting content,interacting with content, joining groups, listing and confirmingattendance at events, checking-in at locations, liking particular pages,creating pages, and performing other tasks that facilitate socialaction. In particular embodiments, networking system 1202 may calculatea coefficient based on the user's actions with particular types ofcontent. The content may be associated with the online social network, athird-party system 1208, or another suitable system. The content mayinclude users, profile pages, posts, news stories, headlines, instantmessages, chat room conversations, emails, advertisements, pictures,video, music, other suitable objects, or any combination thereof.Networking system 1202 may analyze a user's actions to determine whetherone or more of the actions indicate an affinity for subject matter,content, other users, and so forth. As an example and not by way oflimitation, if a user may make frequently posts content related to“coffee” or variants thereof, networking system 1202 may determine theuser has a high coefficient with respect to the concept “coffee”.Particular actions or types of actions may be assigned a higher weightand/or rating than other actions, which may affect the overallcalculated coefficient. As an example and not by way of limitation, if afirst user emails a second user, the weight or the rating for the actionmay be higher than if the first user simply views the user-profile pagefor the second user.

In particular embodiments, networking system 1202 may calculate acoefficient based on the type of relationship between particularobjects. Referencing the social graph 1300, networking system 1202 mayanalyze the number and/or type of edges 1306 connecting particular usernodes 1302 and concept nodes 1304 when calculating a coefficient. As anexample and not by way of limitation, user nodes 1302 that are connectedby a spouse-type edge (representing that the two users are married) maybe assigned a higher coefficient than a user node 1302 that areconnected by a friend-type edge. In other words, depending upon theweights assigned to the actions and relationships for the particularuser, the overall affinity may be determined to be higher for contentabout the user's spouse than for content about the user's friend. Inparticular embodiments, the relationships a user has with another objectmay affect the weights and/or the ratings of the user's actions withrespect to calculating the coefficient for that object. As an exampleand not by way of limitation, if a user is tagged in first photo, butmerely likes a second photo, networking system 1202 may determine thatthe user has a higher coefficient with respect to the first photo thanthe second photo because having a tagged-in-type relationship withcontent may be assigned a higher weight and/or rating than having alike-type relationship with content. In particular embodiments,networking system 1202 may calculate a coefficient for a first userbased on the relationship one or more second users have with aparticular object. In other words, the connections and coefficientsother users have with an object may affect the first user's coefficientfor the object. As an example and not by way of limitation, if a firstuser is connected to or has a high coefficient for one or more secondusers, and those second users are connected to or have a highcoefficient for a particular object, networking system 1202 maydetermine that the first user should also have a relatively highcoefficient for the particular object. In particular embodiments, thecoefficient may be based on the degree of separation between particularobjects. The lower coefficient may represent the decreasing likelihoodthat the first user will share an interest in content objects of theuser that is indirectly connected to the first user in the social graph1300. As an example and not by way of limitation, social-graph entitiesthat are closer in the social graph 1300 (i.e., fewer degrees ofseparation) may have a higher coefficient than entities that are furtherapart in the social graph 1300.

In particular embodiments, networking system 1202 may calculate acoefficient based on location information. Objects that aregeographically closer to each other may be considered to be morerelated, or of more interest, to each other than more distant objects.In particular embodiments, the coefficient of a user towards aparticular object may be based on the proximity of the object's locationto a current location associated with the user (or the location of aclient device 1206 of the user). A first user may be more interested inother users or concepts that are closer to the first user. As an exampleand not by way of limitation, if a user is one mile from an airport andtwo miles from a gas station, networking system 1202 may determine thatthe user has a higher coefficient for the airport than the gas stationbased on the proximity of the airport to the user.

In particular embodiments, networking system 1202 may perform particularactions with respect to a user based on coefficient information.Coefficients may be used to predict whether a user will perform aparticular action based on the user's interest in the action. Acoefficient may be used when generating or presenting any type ofobjects to a user, such as advertisements, search results, news stories,media, messages, notifications, or other suitable objects. Thecoefficient may also be utilized to rank and order such objects, asappropriate. In this way, networking system 1202 may provide informationthat is relevant to user's interests and current circumstances,increasing the likelihood that they will find such information ofinterest. In particular embodiments, networking system 1202 may generatecontent based on coefficient information. Content objects may beprovided or selected based on coefficients specific to a user. As anexample and not by way of limitation, the coefficient may be used togenerate media for the user, where the user may be presented with mediafor which the user has a high overall coefficient with respect to themedia object. As another example and not by way of limitation, thecoefficient may be used to generate advertisements for the user, wherethe user may be presented with advertisements for which the user has ahigh overall coefficient with respect to the advertised object. Inparticular embodiments, networking system 1202 may generate searchresults based on coefficient information. Search results for aparticular user may be scored or ranked based on the coefficientassociated with the search results with respect to the querying user. Asan example and not by way of limitation, search results corresponding toobjects with higher coefficients may be ranked higher on asearch-results page than results corresponding to objects having lowercoefficients.

In particular embodiments, networking system 1202 may calculate acoefficient in response to a request for a coefficient from a particularsystem or process. To predict the likely actions a user may take (or maybe the subject of) in a given situation, any process may request acalculated coefficient for a user. The request may also include a set ofweights to use for various factors used to calculate the coefficient.This request may come from a process running on the online socialnetwork, from a third-party system 1208 (e.g., via an API or othercommunication channel), or from another suitable system. In response tothe request, networking system 1202 may calculate the coefficient (oraccess the coefficient information if it has previously been calculatedand stored). In particular embodiments, networking system 1202 maymeasure an affinity with respect to a particular process. Differentprocesses (both internal and external to the online social network) mayrequest a coefficient for a particular object or set of objects.Networking system 1202 may provide a measure of affinity that isrelevant to the particular process that requested the measure ofaffinity. In this way, each process receives a measure of affinity thatis tailored for the different context in which the process will use themeasure of affinity.

In connection with social-graph affinity and affinity coefficients,particular embodiments may utilize one or more systems, components,elements, functions, methods, operations, or steps disclosed in U.S.patent application Ser. No. 11/503,093, filed 11 Aug. 2006, U.S. patentapplication Ser. No. 12/977,027, filed 22 Dec. 2010, U.S. patentapplication Ser. No. 12/978,265, filed 23 Dec. 2010, and U.S. patentapplication Ser. No. 13/632,869, field 1 Oct. 2012, each of which isincorporated by reference.

In particular embodiments, one or more of the content objects of theonline social network may be associated with a privacy setting. Theprivacy settings (or “access settings”) for an object may be stored inany suitable manner, such as, for example, in association with theobject, in an index on an authorization server, in another suitablemanner, or any combination thereof. A privacy setting of an object mayspecify how the object (or particular information associated with anobject) can be accessed (e.g., viewed or shared) using the online socialnetwork. Where the privacy settings for an object allow a particularuser to access that object, the object may be described as being“visible” with respect to that user. As an example and not by way oflimitation, a user of the online social network may specify privacysettings for a user-profile page identify a set of users that may accessthe work experience information on the user-profile page, thus excludingother users from accessing the information. In particular embodiments,the privacy settings may specify a “blocked list” of users that shouldnot be allowed to access certain information associated with the object.In other words, the blocked list may specify one or more users orentities for which an object is not visible. As an example and not byway of limitation, a user may specify a set of users that may not accessphotos albums associated with the user, thus excluding those users fromaccessing the photo albums (while also possibly allowing certain usersnot within the set of users to access the photo albums). In particularembodiments, privacy settings may be associated with particularsocial-graph elements. Privacy settings of a social-graph element, suchas a node or an edge, may specify how the social-graph element,information associated with the social-graph element, or content objectsassociated with the social-graph element can be accessed using theonline social network. As an example and not by way of limitation, aparticular concept node 1304 corresponding to a particular photo mayhave a privacy setting specifying that the photo may only be accessed byusers tagged in the photo and their friends. In particular embodiments,privacy settings may allow users to opt in or opt out of having theiractions logged by networking system 1202 or shared with other systems(e.g., third-party system 1208). In particular embodiments, the privacysettings associated with an object may specify any suitable granularityof permitted access or denial of access. As an example and not by way oflimitation, access or denial of access may be specified for particularusers (e.g., only me, my roommates, and my boss), users within aparticular degrees-of-separation (e.g., friends, or friends-of-friends),user groups (e.g., the gaming club, my family), user networks (e.g.,employees of particular employers, students or alumni of particularuniversity), all users (“public”), no users (“private”), users ofthird-party systems 1208, particular applications (e.g., third-partyapplications, external websites), other suitable users or entities, orany combination thereof. Although this disclosure describes usingparticular privacy settings in a particular manner, this disclosurecontemplates using any suitable privacy settings in any suitable manner.

In particular embodiments, one or more servers may beauthorization/privacy servers for enforcing privacy settings. Inresponse to a request from a user (or other entity) for a particularobject stored in a data store, networking system 1202 may send a requestto the data store for the object. The request may identify the userassociated with the request and may only be sent to the user (or aclient device 1206 of the user) if the authorization server determinesthat the user is authorized to access the object based on the privacysettings associated with the object. If the requesting user is notauthorized to access the object, the authorization server may preventthe requested object from being retrieved from the data store, or mayprevent the requested object from be sent to the user. In the searchquery context, an object may only be generated as a search result if thequerying user is authorized to access the object. In other words, theobject must have a visibility that is visible to the querying user. Ifthe object has a visibility that is not visible to the user, the objectmay be excluded from the search results. Although this disclosuredescribes enforcing privacy settings in a particular manner, thisdisclosure contemplates enforcing privacy settings in any suitablemanner.

The foregoing specification is described with reference to specificexemplary embodiments thereof. Various embodiments and aspects of thedisclosure are described with reference to details discussed herein, andthe accompanying drawings illustrate the various embodiments. Thedescription above and drawings are illustrative and are not to beconstrued as limiting. Numerous specific details are described toprovide a thorough understanding of various embodiments.

The additional or alternative embodiments may be embodied in otherspecific forms without departing from its spirit or essentialcharacteristics. The described embodiments are to be considered in allrespects only as illustrative and not restrictive. The scope of theinvention is, therefore, indicated by the appended claims rather than bythe foregoing description. All changes that come within the meaning andrange of equivalency of the claims are to be embraced within theirscope.

What is claimed is:
 1. A system comprising at least one processor and atleast one non-transitory computer readable storage medium storinginstructions that, when executed by the at least one processor, causethe system to: utilize a machine learning model to generate predictedcampaign rules for a content distribution campaign, the predictedcampaign rules comprising a plurality of predicted conditions and aplurality of predicted actions for the plurality of predictedconditions; monitor activity of the content distribution campaign toautomatically detect whether a first condition from the plurality ofpredicted conditions has been triggered, the first conditioncorresponding to a first action from the plurality of predicted actions;and in response to detecting that the first condition has beentriggered, automatically modify the content distribution campaignaccording to the first action.
 2. The system of claim 1, further storinginstructions that, when executed by the at least one processor, causethe system to provide, for display within a user interface, theplurality of predicted conditions and the plurality of predicted actionsfor the plurality of predicted conditions.
 3. The system of claim 1,wherein the first condition comprises a budget threshold, a costthreshold, or an impression threshold.
 4. The system of claim 1, whereinthe first action comprises increasing a keyword bid amount, decreasing akeyword bid amount, increasing a budget amount, or decreasing a budgetamount.
 5. The system of claim 1, further storing instructions that,when executed by the at least one processor, cause the system to trainthe machine learning model to generate the predicted campaign rulesbased on historical content distribution campaigns.
 6. The system ofclaim 5 wherein training the machine learning model comprises:identifying a performance metric of the historical content distributioncampaigns and a corresponding performance metric of the contentdistribution campaign; and modifying parameters of the machine learningmodel based on a comparison of the performance metric of the historicalcontent distribution campaigns and the performance metric of the contentdistribution campaign.
 7. The system of claim 6 wherein the performancemetric comprises one or more of an amount of clicks, an amount ofimpressions, or an amount spent.
 8. The system of claim 1, furtherstoring instructions that, when executed by the at least one processor,cause the system to automatically execute the modified contentdistribution campaign.
 9. The system of claim 8, further storinginstructions that, when executed by the at least one processor, causethe system to provide a notification to a device of a publisherassociated with the content distribution campaign.
 10. The system ofclaim 1, further storing instructions that, when executed by the atleast one processor, cause the system to monitor user interactions withthe content distribution campaign.
 11. The system of claim 10 whereinthe user interactions comprise one or more of a user selecting digitalcontent, a user visiting a website corresponding to the digital content,a user purchasing an item corresponding to the content distributioncampaign, or a user viewing the digital content.
 12. The system of claim10, further storing instructions that, when executed by the at least oneprocessor, cause the system to bill a publisher associated with thecontent distribution campaign based on the monitored user interactions.13. The system of claim 1, further storing instructions that, whenexecuted by the at least one processor, cause the system to monitoractivity corresponding to the content distribution campaign to detect animpression opportunity.
 14. The system of claim 13, further storinginstructions that, when executed by the at least one processor, causethe system to execute an auction for the impression opportunity.
 15. Thesystem of claim 1, wherein the machine learning model comprises one ormore of decision trees, support vector machines, linear regression,logistic regression, Bayesian networks, clustering, K-nearest neighbors,K-means, random forest learning, dimensionality reduction algorithms,boosting algorithms, artificial neural networks, or deep learning.
 16. Amethod comprising: utilizing a machine learning model to generatepredicted campaign rules for a content distribution campaign, thepredicted campaign rules comprising a plurality of predicted conditionsand a plurality of predicted actions for the plurality of predictedconditions; monitoring activity of the content distribution campaign toautomatically detect whether a first condition from the plurality ofpredicted conditions has been triggered, the first conditioncorresponding to a first action from the plurality of predicted actions;and in response to detecting that the first condition has beentriggered, automatically modifying the content distribution campaignaccording to the first action.
 17. A system comprising at least oneprocessor and at least one non-transitory computer readable storagemedium storing instructions that, when executed by the at least oneprocessor, cause the system to: identify one or more features of adigital distribution campaign; utilize a machine learning model with theone or more features of the digital distribution campaign to determineone or more predicted campaign rules comprising one or more predictedcampaign conditions and corresponding predicted action recommendationsthat increase a likelihood of a user population engaging with a contentdistribution campaign based on one or more monitored user activities;and based on the determined one or more predicted campaign rules, modifythe content distribution campaign by applying an action from thepredicted action recommendations when a campaign condition from the oneor more predicted campaign conditions is triggered by the one or moremonitored user activities, wherein the action comprises increasing ordecreasing a bid amount for the content distribution campaign.
 18. Thesystem of claim 17 wherein modifying the content distribution campaigncomprises increasing a keyword bid in response to the machine learningmodel determining that the user population is more likely to engage withthe content distribution campaign.
 19. The system of claim 17 whereinmodifying the content distribution campaign comprises lowering a keywordbid in response to the machine learning model determining that the userpopulation is less likely to engage with the content distributioncampaign.
 20. The system of claim 17, wherein the campaign conditioncomprises a budget threshold, a cost threshold, or an impressionthreshold.